Stories with Tag Knowledge Representation

• Questions for William J. Rapaport

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  Posted: 2025-03-06 18:24:37

  Verbose tl;dr

  This Google Form poses a series of questions to William J. Rapaport regarding his views on the possibility of conscious AI. It probes his criteria for consciousness, asking him to clarify the necessary and sufficient conditions for a system to be considered conscious, and how he would test for them. The questions specifically explore his stance on computational theories of mind, the role of embodiment, and the relevance of subjective experience. Furthermore, it asks about his interpretation of specific thought experiments related to consciousness and AI, including the Chinese Room Argument, and solicits his opinions on the potential implications of creating conscious machines.

  This Google Form presents a series of inquiries directed towards William J. Rapaport, a distinguished figure in the fields of computer science, philosophy, and linguistics, particularly known for his work on computational theories of cognition and consciousness. The form's purpose is to solicit Professor Rapaport's expert perspectives on a diverse range of topics centered around the philosophical implications of artificial intelligence, the nature of consciousness, and the potential for artificial general intelligence (AGI).

  The questionnaire begins with an acknowledgement of Professor Rapaport's extensive contributions to the field, specifically referencing his 1988 paper titled "Syntactic Semantics: Foundations of Computational Natural-Language Understanding." Following this preamble, the form proceeds to pose a series of carefully crafted questions, each designed to elicit nuanced insights into Professor Rapaport's current thinking on these complex issues.

  A significant portion of the questions delve into the very definition of consciousness, exploring its potential measurability and the implications of its presence or absence in artificial systems. The form probes Professor Rapaport's views on the necessary and sufficient conditions for consciousness, questioning whether current computational models adequately capture the essence of subjective experience. It also inquires about his opinions on the possibility of definitively proving or disproving the existence of consciousness in any entity, be it biological or artificial.

  Furthermore, the questionnaire explores the potential for artificial systems to achieve genuine understanding, as opposed to merely simulating it. It asks Professor Rapaport to elaborate on the distinctions between understanding and other cognitive processes, and to address the challenges inherent in assessing true comprehension in machines. The form also touches upon the concept of intentionality, a crucial aspect of mental states that refers to their "aboutness" or directedness towards something, and its role in defining intelligence and consciousness.

  Finally, the questionnaire addresses broader philosophical questions related to the nature of reality and the potential impact of advanced AI. It inquires about Professor Rapaport's perspectives on the implications of artificial general intelligence for humanity, and seeks his thoughts on the potential for AI to reshape our understanding of ourselves and the world around us. The overall tone of the form is one of respectful inquiry, seeking to engage with Professor Rapaport's expertise and contribute to a deeper understanding of these profound and multifaceted issues.

  • William J. Rapaport
  • Interview
  • questions
  • Cognitive Science
  • philosophy of mind
  • artificial intelligence
  • Computer Science
  • Philosophy
  • Consciousness
  • computation
  • natural language processing
  • Knowledge Representation
  • Semantics

  Summary of Comments ( 2 )
  https://news.ycombinator.com/item?id=43283367

  Verbose tl;dr

  The Hacker News comments on the "Questions for William J. Rapaport" post are sparse and don't offer much substantive discussion. A couple of users express skepticism about the value or seriousness of the questionnaire, questioning its purpose and suggesting it might be a student project or even a prank. One commenter mentions Rapaport's work in cognitive science and AI, suggesting a potential connection to the topic of consciousness. However, there's no in-depth engagement with the questionnaire itself or Rapaport's potential responses. Overall, the comment section provides little insight beyond a general sense of skepticism.

  The Hacker News post titled "Questions for William J. Rapaport" links to a Google Form intended for attendees of a talk by Professor Rapaport on "How to Write a Philosophy Paper" to submit questions beforehand. The discussion on Hacker News is minimal, with only two comments, neither directly addressing the linked form or Professor Rapaport's talk. Therefore, it's impossible to summarize compelling comments related to the topic, as none exist.

  The first comment simply expresses the user's enjoyment of the Google Docs preview of the form, highlighting the visual appearance of the embedded form within the Hacker News platform. It does not engage with the subject matter of philosophical paper writing.

  The second comment is entirely unrelated to the original post. It consists of a single link to an external resource about LaTeX, a typesetting system often used for academic writing. While LaTeX could be relevant to writing philosophy papers, the comment offers no context or explanation connecting the two, making it difficult to interpret as a substantive contribution to the discussion.

  In summary, the Hacker News thread lacks substantial engagement with the topic of writing philosophy papers or the questions for Professor Rapaport. The few comments present are either superficial observations about the form's presentation or tangentially related links without accompanying explanation.

• Translating Natural Language to First-Order Logic for Logical Fallacy Detection

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  Posted: 2025-03-04 17:36:23

  Verbose tl;dr

  This paper explores using first-order logic (FOL) to detect logical fallacies in natural language arguments. The authors propose a novel approach that translates natural language arguments into FOL representations, leveraging semantic role labeling and a defined set of predicates to capture argument structure. This structured representation allows for the application of automated theorem provers to evaluate the validity of the arguments, thus identifying potential fallacies. The research demonstrates improved performance compared to existing methods, particularly in identifying fallacies related to invalid argument structure, while acknowledging limitations in handling complex linguistic phenomena and the need for further refinement in the translation process. The proposed system provides a promising foundation for automated fallacy detection and contributes to the broader field of argument mining.

  The arXiv preprint "Translating Natural Language to First-Order Logic for Logical Fallacy Detection" by Liu et al. explores a novel approach to identifying logical fallacies within natural language arguments. The authors posit that current methods for fallacy detection, which largely rely on surface-level linguistic features or shallow semantic analysis, are insufficient for capturing the underlying logical structure necessary for robust fallacy identification. They propose instead a method grounded in formal logic, specifically first-order logic (FOL), which allows for a more rigorous and precise representation of argumentative structures.

  The core of their proposed methodology lies in translating natural language arguments into FOL representations. This translation process involves several intricate steps. First, the argumentative text is parsed to identify individual premises and the conclusion. Subsequently, these components are subjected to semantic parsing, transforming them into logical forms expressible within FOL. This necessitates the identification of entities, predicates, and quantifiers present in the natural language, and their subsequent mapping to corresponding elements within the FOL framework. The authors acknowledge the inherent complexity and ambiguity of natural language, which poses a significant challenge for accurate translation. To address this, they employ a combination of existing semantic parsing techniques and introduce novel strategies tailored to the specific requirements of fallacy detection.

  Once the argument is represented in FOL, the authors leverage the power of automated theorem provers to assess the argument's validity. By attempting to prove the conclusion from the premises within the FOL framework, they can determine whether the argument is logically sound. If the conclusion cannot be derived from the premises, this suggests the potential presence of a logical fallacy. However, the mere failure of a proof does not definitively indicate a fallacy; it could simply reflect limitations in the translation process or the theorem prover's capabilities.

  Therefore, the authors introduce a further layer of analysis based on fallacy templates. These templates represent common logical fallacies, such as ad hominem, straw man, or false dilemma, formalized within the FOL framework. By matching the FOL representation of the argument against these pre-defined fallacy templates, the system can identify instances where the argument's structure aligns with a known fallacious pattern. This template-matching approach provides a more targeted and nuanced mechanism for fallacy detection, going beyond the simple binary classification of valid or invalid.

  The paper details experiments conducted on established fallacy datasets, comparing their proposed FOL-based method against existing state-of-the-art techniques. The authors report promising results, demonstrating that their approach achieves improved accuracy in identifying various types of logical fallacies. They further analyze the strengths and limitations of their methodology, acknowledging the ongoing challenges in accurately translating complex natural language arguments into FOL and the need for more comprehensive fallacy templates. The research concludes by emphasizing the potential of FOL-based approaches for advancing the field of automated logical fallacy detection and suggests future research directions, such as incorporating more sophisticated semantic parsing techniques and expanding the library of formalized fallacy templates.

  • natural language processing
  • NLP
  • First-Order Logic
  • FOL
  • Logical Fallacies
  • Fallacy Detection
  • Automated Reasoning
  • Knowledge Representation
  • theorem proving
  • Logic
  • artificial intelligence
  • AI
  • machine learning
  • translation
  • formal methods

  Summary of Comments ( 68 )
  https://news.ycombinator.com/item?id=43257719

  Verbose tl;dr

  Hacker News users discussed the potential and limitations of using first-order logic (FOL) for fallacy detection as described in the linked paper. Some praised the approach for its rigor and potential to improve reasoning in AI, while also acknowledging the inherent difficulty of translating natural language to FOL perfectly. Others questioned the practical applicability, citing the complexity and ambiguity of natural language as major obstacles, and suggesting that statistical/probabilistic methods might be more robust. The difficulty of scoping the domain knowledge necessary for FOL translation was also brought up, with some pointing out the need for extensive, context-specific knowledge bases. Finally, several commenters highlighted the limitations of focusing solely on logical fallacies for detecting flawed reasoning, suggesting that other rhetorical tactics and nuances should also be considered.

  The Hacker News post titled "Translating Natural Language to First-Order Logic for Logical Fallacy Detection" (linking to arXiv paper 2405.02318) has a modest number of comments, sparking a discussion around the practicality and challenges of using formal logic for fallacy detection.

  One commenter expresses skepticism about the real-world applicability of this approach. They argue that logical fallacies in everyday discourse often hinge on implicit premises and contextual nuances that are difficult to capture in formal logic. They suggest that focusing on these implicit elements, which the current approach seems to bypass, is crucial for effective fallacy detection. This commenter also points out the challenge of translating the richness and ambiguity of natural language into the rigid structure of first-order logic, questioning the feasibility of achieving high accuracy in this translation process.

  Another commenter builds on this skepticism by highlighting the issue of ambiguity inherent in natural language. They provide the example of the phrase "most people," which can have different interpretations depending on the context, and how formalizing such a phrase would necessitate making assumptions about the intended quantifier. This emphasizes the difficulty of creating a universally applicable system, as the interpretation of such phrases would need to be tailored to specific domains or contexts.

  A different commenter suggests an alternative perspective, mentioning a different approach to fallacy detection that utilizes large language models (LLMs). They point to a paper where LLMs are used to identify fallacies without explicit formalization. This comment implies that perhaps direct application of statistical methods via LLMs could be a more promising avenue for fallacy detection than attempting the complex task of translating natural language into formal logic.

  Another commenter echoes the concern about the limitations of formal logic in capturing the subtleties of natural language arguments, particularly those involving informal fallacies. They also touch upon the issue of computational complexity associated with logical reasoning, suggesting that practical implementations might face performance bottlenecks.

  Finally, one commenter asks a clarifying question about the specific types of logical fallacies the research addresses, indicating a desire to understand the scope and limitations of the proposed approach. This highlights the importance of clearly defining the target fallacies when evaluating the effectiveness of such systems.

  In summary, the comments largely express reservations about the practicality of the approach outlined in the linked paper, focusing on the difficulties of translating nuanced natural language into formal logic and the potential computational complexities. Alternatives using LLMs are suggested, and the need for careful consideration of the target fallacies is highlighted.

• PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models

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  Posted: 2025-02-09 18:14:01

  Verbose tl;dr

  The paper "PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models" introduces "GSM8K," a dataset of 8.5K grade school math word problems designed to evaluate the reasoning and problem-solving abilities of large language models (LLMs). The authors argue that existing benchmarks often rely on specialized knowledge or easily-memorized patterns, while GSM8K focuses on compositional reasoning using basic arithmetic operations. They demonstrate that even the most advanced LLMs struggle with these seemingly simple problems, significantly underperforming human performance. This highlights the gap between current LLMs' ability to manipulate language and their true understanding of underlying concepts, suggesting future research directions focused on improving reasoning and problem-solving capabilities.

  The preprint, "PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models," introduces a novel benchmark dataset called FOLIO, specifically designed to assess the complex reasoning capabilities of Large Language Models (LLMs) without necessitating specialized, PhD-level knowledge. The authors argue that existing benchmarks often inadvertently test for factual recall of esoteric information, rather than the core reasoning skills that are fundamental to general intelligence. They posit that true reasoning prowess lies in the ability to derive logical conclusions from presented information, irrespective of the specific domain.

  FOLIO comprises a collection of intricate reasoning puzzles encompassing various domains such as mathematics, physics, and economics. Crucially, however, all necessary information for solving these puzzles is explicitly provided within the problem description itself. This eliminates the reliance on pre-existing knowledge and ensures that the LLM's performance reflects its capacity for logical deduction and inference, rather than its ability to retrieve stored facts. The puzzles are structured with a clear separation between the given information, the question being posed, and multiple-choice answer options. This structured format facilitates automated evaluation and comparison across different LLM architectures.

  The authors meticulously constructed FOLIO to minimize the potential for shortcut solutions. They employed strategies such as paraphrasing and diversifying the presentation of information to prevent LLMs from exploiting superficial patterns in the data. Furthermore, they incorporated "adversarial" examples designed to specifically challenge common weaknesses observed in current LLMs, such as overreliance on surface-level cues or a propensity for generating plausible-sounding but logically incorrect answers.

  The paper details the performance of several prominent LLMs on the FOLIO benchmark. The results demonstrate a significant gap between current LLM capabilities and human-level performance on these reasoning tasks. This highlights the limitations of contemporary LLMs in handling complex logical deductions, even when all necessary information is readily available. The authors suggest that FOLIO provides a valuable tool for future research aimed at developing more robust and generally intelligent LLMs, focusing on the enhancement of genuine reasoning skills rather than merely accumulating vast amounts of factual knowledge. They further argue that FOLIO offers a more accurate assessment of the fundamental reasoning ability of LLMs, separating it from the confounding factor of factual recall often present in existing benchmarks. This separation provides a clearer picture of the progress and challenges in developing truly intelligent systems.

  • Large Language Models
  • LLMs
  • Reasoning
  • natural language processing
  • NLP
  • artificial intelligence
  • AI
  • Knowledge Representation
  • Problem Solving
  • benchmarking
  • Evaluation
  • PhD
  • Expertise
  • Common Sense
  • Cognitive Science
  • machine learning

  Summary of Comments ( 24 )
  https://news.ycombinator.com/item?id=42992336

  Verbose tl;dr

  HN users generally found the paper's reasoning challenge interesting, but questioned its practicality and real-world relevance. Some pointed out that the challenge focuses on a niche area of knowledge (PhD-level scientific literature), while others doubted its ability to truly test reasoning beyond pattern matching. A few commenters discussed the potential for LLMs to assist with literature review and synthesis, but skepticism remained about whether these models could genuinely understand and contribute to scientific discourse at a high level. The core issue raised was whether solving contrived challenges translates to real-world problem-solving abilities, with several commenters suggesting that the focus should be on more practical applications of LLMs.

  The Hacker News post titled "PhD Knowledge Not Required: A Reasoning Challenge for Large Language Models" (https://news.ycombinator.com/item?id=4292336) links to a preprint paper exploring reasoning challenges for LLMs. The discussion on Hacker News is relatively brief, with a few comments focusing on specific aspects of the paper's approach and findings.

  One commenter points out that the benchmark presented, while seemingly simple, proves surprisingly difficult for current LLMs, suggesting the gap between human-like reasoning and current AI capabilities remains significant, even in seemingly straightforward scenarios. They highlight the importance of developing benchmarks that accurately reflect real-world reasoning tasks.

  Another comment expresses skepticism about the chosen evaluation metric, arguing that focusing solely on answer accuracy might not fully capture the nuances of reasoning. They suggest that evaluating the process of reasoning, rather than just the final answer, could provide more valuable insights into the LLM's capabilities and limitations. This commenter also mentions the potential for LLMs to exploit statistical correlations in the data, achieving high accuracy without genuinely understanding the underlying reasoning principles.

  A further comment questions the paper's claim that these tasks don't require specialized PhD-level knowledge. While acknowledging that the problems themselves may appear simple on the surface, they suggest that the type of reasoning required, and the ability to generalize from limited examples, might indeed draw upon more sophisticated cognitive processes akin to those developed through specialized education. They don't necessarily disagree with the overall premise of the paper but offer a nuanced perspective on the nature of the "knowledge" involved.

  There's a brief exchange about the applicability of chain-of-thought prompting, with one commenter noting its effectiveness in some cases but acknowledging that the paper demonstrates its limitations in these specific reasoning challenges.

  Overall, the comments on Hacker News provide a concise discussion of the paper's core ideas, raising important points about evaluation metrics, the nature of reasoning, and the gap between current LLM capabilities and human-level performance. The comments do not constitute an extensive or in-depth analysis but offer valuable perspectives on the challenges of evaluating and improving reasoning abilities in LLMs.