Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering

• URL: http://arxiv.org/abs/2410.15440v1
• Date: Sun, 20 Oct 2024 16:21:25 GMT
• Title: Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering
• Authors: Yanggyu Lee, Jihie Kim,
• Abstract summary: We present a new approach for evaluating semanticencies of Large Language Model (LLM) Our approach evaluates whether LLM re-sponses are semantically congruent for a given question, recognizing that as syntactically different sentences may convey the same meaning. Using the TruthfulQA dataset to assess LLM responses, the study induces N re-sponses per question and clusters semantically equivalent sentences to measure semantic consistency across 37 categories.
• Score: 1.9214041945441436
• License:
• Abstract: In the realm of Large Language Model (LLM) functionalities, providing reliable information is paramount, yet reports suggest that LLM outputs lack consistency. This inconsistency, often at-tributed to randomness in token sampling, under-mines user trust as it leads to varying responses even for identical queries. In this paper, we present a new approach for evaluating semantic consistencies of LLM including comparison of alternative tech-niques. Our approach evaluates whether LLM re-sponses are semantically congruent for a given question, recognizing that as syntactically different sentences may convey the same meaning. Here-tofore, To enhance LLM consistency, two main approaches have been explored: Leverage external knowledge as context like the RAG pattern or use Zero-shot-CoT to improve performance of LLM itself. We apply our evaluation approach to these techniques, and demonstrate to compare the im-pact of these methods on LLM response con-sistency across different domains of question an-swering tasks. Using the TruthfulQA dataset to assess LLM responses, the study induces N re-sponses per question from the LLM and clusters semantically equivalent sentences to measure semantic consistency across 37 categories. Through this, it quantitatively analyzes the effectiveness of the aforementioned methods in improving LLM performance before and after their adoption.

Related papers

• Self-Calibrated Listwise Reranking with Large Language Models [137.6557607279876]
  Large language models (LLMs) have been employed in reranking tasks through a sequence-to-sequence approach. This reranking paradigm requires a sliding window strategy to iteratively handle larger candidate sets. We propose a novel self-calibrated listwise reranking method, which aims to leverage LLMs to produce global relevance scores for ranking.
  arXiv  Detail & Related papers  (2024-11-07T10:31:31Z)
• Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement [51.601916604301685]
  Large language models (LLMs) generate content that can undermine trust in online discourse. Current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-AI collaboration. To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content.
  arXiv  Detail & Related papers  (2024-10-18T08:14:10Z)
• Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints [20.844061807562436]
  We propose SENSE, a novel prompting approach that embeds semantic hints within the prompt. Experiments show that SENSE consistently improves LLMs' performance across various tasks.
  arXiv  Detail & Related papers  (2024-09-22T14:35:09Z)
• The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism [39.392450788666814]
  Current evaluations of large language models (LLMs) often overlook non-determinism. greedy decoding generally outperforms sampling methods for most evaluated tasks. Smaller LLMs can match or surpass larger models such as GPT-4-Turbo.
  arXiv  Detail & Related papers  (2024-07-15T06:12:17Z)
• CSS: Contrastive Semantic Similarity for Uncertainty Quantification of LLMs [1.515687944002438]
  We propose Contrastive Semantic Similarity, a module to obtain similarity features for measuring uncertainty for text pairs. We conduct extensive experiments with three large language models (LLMs) on several benchmark question-answering datasets. Results show that our proposed method performs better in estimating reliable responses of LLMs than comparable baselines.
  arXiv  Detail & Related papers  (2024-06-05T11:35:44Z)
• Decompose and Aggregate: A Step-by-Step Interpretable Evaluation Framework [75.81096662788254]
  Large Language Models (LLMs) are scalable and economical evaluators. The question of how reliable these evaluators are has emerged as a crucial research question. We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
  arXiv  Detail & Related papers  (2024-05-24T08:12:30Z)
• Improve Temporal Awareness of LLMs for Sequential Recommendation [61.723928508200196]
  Large language models (LLMs) have demonstrated impressive zero-shot abilities in solving a wide range of general-purpose tasks. LLMs fall short in recognizing and utilizing temporal information, rendering poor performance in tasks that require an understanding of sequential data. We propose three prompting strategies to exploit temporal information within historical interactions for LLM-based sequential recommendation.
  arXiv  Detail & Related papers  (2024-05-05T00:21:26Z)
• PiCO: Peer Review in LLMs based on the Consistency Optimization [19.130941716491716]
  We use peer-review mechanisms to measure large language models (LLMs) automatically. We formalize it as a constrained optimization problem, intending to maximize the consistency of each LLM's capabilities and scores. We propose three metrics called PEN, CIN, and LIS to evaluate the gap in aligning human rankings.
  arXiv  Detail & Related papers  (2024-02-02T18:49:26Z)
• Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves [57.974103113675795]
  We present a method named Rephrase and Respond' (RaR) which allows Large Language Models to rephrase and expand questions posed by humans. RaR serves as a simple yet effective prompting method for improving performance. We show that RaR is complementary to the popular Chain-of-Thought (CoT) methods, both theoretically and empirically.
  arXiv  Detail & Related papers  (2023-11-07T18:43:34Z)
• Assessing the Reliability of Large Language Model Knowledge [78.38870272050106]
  Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. How do we evaluate the capabilities of LLMs to consistently produce factually correct answers? We propose MOdel kNowledge relIabiliTy scORe (MONITOR), a novel metric designed to directly measure LLMs' factual reliability.
  arXiv  Detail & Related papers  (2023-10-15T12:40:30Z)
• Semantic Consistency for Assuring Reliability of Large Language Models [9.876355290198639]
  Large Language Models (LLMs) exhibit remarkable fluency and competence across various natural language tasks. We introduce a general measure of semantic consistency, and formulate multiple versions of this metric to evaluate the performance of various LLMs. We propose a novel prompting strategy, called Ask-to-Choose (A2C), to enhance semantic consistency.
  arXiv  Detail & Related papers  (2023-08-17T18:11:33Z)

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.