Reassessing the Role of Chain-of-Thought in Sentiment Analysis: Insights and Limitations

• URL: http://arxiv.org/abs/2501.08641v1
• Date: Wed, 15 Jan 2025 08:07:22 GMT
• Title: Reassessing the Role of Chain-of-Thought in Sentiment Analysis: Insights and Limitations
• Authors: Kaiyuan Zheng, Qinghua Zhao, Lei Li,
• Abstract summary: We investigate whether reasoning techniques can facilitate semantic understanding. We employ chain-of-thought prompting as a reasoning technique. Experiments show that chain-of-thought has a minimal impact on sentiment analysis tasks.
• Score: 6.774149276707635
• License:
• Abstract: The relationship between language and thought remains an unresolved philosophical issue. Existing viewpoints can be broadly categorized into two schools: one asserting their independence, and another arguing that language constrains thought. In the context of large language models, this debate raises a crucial question: Does a language model's grasp of semantic meaning depend on thought processes? To explore this issue, we investigate whether reasoning techniques can facilitate semantic understanding. Specifically, we conceptualize thought as reasoning, employ chain-of-thought prompting as a reasoning technique, and examine its impact on sentiment analysis tasks. The experiments show that chain-of-thought has a minimal impact on sentiment analysis tasks. Both the standard and chain-of-thought prompts focus on aspect terms rather than sentiment in the generated content. Furthermore, counterfactual experiments reveal that the model's handling of sentiment tasks primarily depends on information from demonstrations. The experimental results support the first viewpoint.

Related papers

• A Computationally Grounded Framework for Cognitive Attitudes (extended version) [14.866324473006255]
  We introduce a novel language for reasoning about agents' cognitive attitudes of both epistemic and motivational type. Our language includes five types of modal operators for implicit belief, complete attraction, complete repulsion, realistic attraction and realistic repulsion. We present a dynamic extension of the language that supports reasoning about the effects of belief change operations.
  arXiv  Detail & Related papers  (2024-12-18T17:17:07Z)
• PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis [74.41260927676747]
  This paper bridges the gaps by introducing a multimodal conversational Sentiment Analysis (ABSA) To benchmark the tasks, we construct PanoSent, a dataset annotated both manually and automatically, featuring high quality, large scale, multimodality, multilingualism, multi-scenarios, and covering both implicit and explicit sentiment elements. To effectively address the tasks, we devise a novel Chain-of-Sentiment reasoning framework, together with a novel multimodal large language model (namely Sentica) and a paraphrase-based verification mechanism.
  arXiv  Detail & Related papers  (2024-08-18T13:51:01Z)
• To Word Senses and Beyond: Inducing Concepts with Contextualized Language Models [0.9176056742068812]
  Polysemy and synonymy are two crucial interrelated facets of lexical ambiguity. In this paper, we introduce Concept Induction, the unsupervised task of learning a soft clustering among words. We propose a bi-level approach to Concept Induction that leverages both a local lemma-centric view and a global cross-lexicon view to induce concepts.
  arXiv  Detail & Related papers  (2024-06-28T17:07:06Z)
• Conceptual and Unbiased Reasoning in Language Models [98.90677711523645]
  We propose a novel conceptualization framework that forces models to perform conceptual reasoning on abstract questions. We show that existing large language models fall short on conceptual reasoning, dropping 9% to 28% on various benchmarks. We then discuss how models can improve since high-level abstract reasoning is key to unbiased and generalizable decision-making.
  arXiv  Detail & Related papers  (2024-03-30T00:53:53Z)
• Identifying and interpreting non-aligned human conceptual representations using language modeling [0.0]
  We show that congenital blindness induces conceptual reorganization in both a-modal and sensory-related verbal domains. We find that blind individuals more strongly associate social and cognitive meanings to verbs related to motion. For some verbs, representations of blind and sighted are highly similar.
  arXiv  Detail & Related papers  (2024-03-10T13:02:27Z)
• Contrastive Chain-of-Thought Prompting [74.10511560147293]
  We propose contrastive chain of thought to enhance language model reasoning. Compared to the conventional chain of thought, our approach provides both valid and invalid reasoning demonstrations. Our experiments on reasoning benchmarks demonstrate that contrastive chain of thought can serve as a general enhancement of chain-of-thought prompting.
  arXiv  Detail & Related papers  (2023-11-15T18:54:01Z)
• Implicit Chain of Thought Reasoning via Knowledge Distillation [58.80851216530288]
  Instead of explicitly producing the chain of thought reasoning steps, we use the language model's internal hidden states to perform implicit reasoning. We find that this approach enables solving tasks previously not solvable without explicit chain-of-thought, at a speed comparable to no chain-of-thought.
  arXiv  Detail & Related papers  (2023-11-02T17:59:49Z)
• DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning [89.92601337474954]
  Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations. We introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding.
  arXiv  Detail & Related papers  (2023-06-15T10:41:23Z)
• Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners [75.85554779782048]
  Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous successful applications, the underlying mechanism of such in-context capabilities still remains unclear. In this work, we hypothesize that the learned textitsemantics of language tokens do the most heavy lifting during the reasoning process.
  arXiv  Detail & Related papers  (2023-05-24T07:33:34Z)
• A Quantitative Symbolic Approach to Individual Human Reasoning [0.0]
  We take findings from the literature and show how these, formalized as cognitive principles within a logical framework, can establish a quantitative notion of reasoning. We employ techniques from non-monotonic reasoning and computer science, namely, a solving paradigm called answer set programming (ASP) Finally, we can fruitfully use plausibility reasoning in ASP to test the effects of an existing experiment and explain different majority responses.
  arXiv  Detail & Related papers  (2022-05-10T16:43:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.