Ask Again, Then Fail: Large Language Models' Vacillations in Judgment

• URL: http://arxiv.org/abs/2310.02174v5
• Date: Tue, 11 Jun 2024 15:22:07 GMT
• Title: Ask Again, Then Fail: Large Language Models' Vacillations in Judgment
• Authors: Qiming Xie, Zengzhi Wang, Yi Feng, Rui Xia,
• Abstract summary: We observe that current conversational language models often waver in their judgments when faced with follow-up questions. We introduce a textscFollow-up Questioning Mechanism along with two metrics to quantify this inconsistency. We develop a training-based framework textscUnwavering-FQ that teaches language models to maintain their originally correct judgments.
• Score: 28.74246375289661
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We observe that current conversational language models often waver in their judgments when faced with follow-up questions, even if the original judgment was correct. This wavering presents a significant challenge for generating reliable responses and building user trust. To comprehensively assess this issue, we introduce a \textsc{Follow-up Questioning Mechanism} along with two metrics to quantify this inconsistency, confirming its widespread presence in current language models. To mitigate this issue, we explore various prompting strategies for closed-source models; moreover, we develop a training-based framework \textsc{Unwavering-FQ} that teaches language models to maintain their originally correct judgments through synthesized high-quality preference data. Our experimental results confirm the effectiveness of our framework and its ability to enhance the general capabilities of models.

Related papers

• Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation [12.921225188504643]
  We propose a novel Uncertainty-aware Reward Model (URM) that introduces a robust uncertainty estimation for the quality of paired responses. Empirical results demonstrate significant benefits of incorporating the proposed proxy into language model training.
  arXiv  Detail & Related papers  (2024-05-10T12:14:11Z)
• More Room for Language: Investigating the Effect of Retrieval on Language Models [3.8574940917179164]
  We introduce an 'ideal retrieval' methodology to study these models in a fully controllable setting. We conduct an evaluation to examine how retrieval augmentation affects the behavior of the underlying language model.
  arXiv  Detail & Related papers  (2024-04-16T22:43:48Z)
• Evidence from counterfactual tasks supports emergent analogical reasoning in large language models [3.9189409002585562]
  We report evidence that large language models are capable of solving a wide range of text-based analogy problems in a zero-shot manner. Two recent commentaries have challenged these results, citing evidence from so-called counterfactual' tasks in which the standard sequence of the alphabet is arbitrarily permuted. Here, we reply to these critiques, clarifying some misunderstandings about the test materials used in our original work, and presenting evidence that language models are also capable of generalizing to these new counterfactual task variants.
  arXiv  Detail & Related papers  (2024-04-14T21:51:02Z)
• Context versus Prior Knowledge in Language Models [49.17879668110546]
  Language models often need to integrate prior knowledge learned during pretraining and new information presented in context. We propose two mutual information-based metrics to measure a model's dependency on a context and on its prior about an entity.
  arXiv  Detail & Related papers  (2024-04-06T13:46:53Z)
• Calibrating the Confidence of Large Language Models by Eliciting Fidelity [52.47397325111864]
  Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless. Post-alignment, these language models often exhibit overconfidence, where the expressed confidence does not accurately calibrate with their correctness rate. We propose a plug-and-play method to estimate the confidence of language models.
  arXiv  Detail & Related papers  (2024-04-03T11:36:12Z)
• Fine-tuning Language Models for Factuality [96.5203774943198]
  Large pre-trained language models (LLMs) have led to their widespread use, sometimes even as a replacement for traditional search engines. Yet language models are prone to making convincing but factually inaccurate claims, often referred to as 'hallucinations' In this work, we fine-tune language models to be more factual, without human labeling.
  arXiv  Detail & Related papers  (2023-11-14T18:59:15Z)
• Towards Improving Faithfulness in Abstractive Summarization [37.19777407790153]
  We propose a Faithfulness Enhanced Summarization model (FES) to improve fidelity in abstractive summarization. Our model outperforms strong baselines in experiments on CNN/DM and XSum.
  arXiv  Detail & Related papers  (2022-10-04T19:52:09Z)
• Learning from Lexical Perturbations for Consistent Visual Question Answering [78.21912474223926]
  Existing Visual Question Answering (VQA) models are often fragile and sensitive to input variations. We propose a novel approach to address this issue based on modular networks, which creates two questions related by linguistic perturbations. We also present VQA Perturbed Pairings (VQA P2), a new, low-cost benchmark and augmentation pipeline to create controllable linguistic variations.
  arXiv  Detail & Related papers  (2020-11-26T17:38:03Z)
• Knowledge-Grounded Dialogue Generation with Pre-trained Language Models [74.09352261943911]
  We study knowledge-grounded dialogue generation with pre-trained language models. We propose equipping response generation defined by a pre-trained language model with a knowledge selection module.
  arXiv  Detail & Related papers  (2020-10-17T16:49:43Z)
• InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective [84.78604733927887]
  Large-scale language models such as BERT have achieved state-of-the-art performance across a wide range of NLP tasks. Recent studies show that such BERT-based models are vulnerable facing the threats of textual adversarial attacks. We propose InfoBERT, a novel learning framework for robust fine-tuning of pre-trained language models.
  arXiv  Detail & Related papers  (2020-10-05T20:49:26Z)

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.