Know Your Mistakes: Towards Preventing Overreliance on Task-Oriented Conversational AI Through Accountability Modeling

• URL: http://arxiv.org/abs/2501.10316v2
• Date: Wed, 19 Feb 2025 00:39:39 GMT
• Title: Know Your Mistakes: Towards Preventing Overreliance on Task-Oriented Conversational AI Through Accountability Modeling
• Authors: Suvodip Dey, Yi-Jyun Sun, Gokhan Tur, Dilek Hakkani-Tur,
• Abstract summary: We propose an accountability model for task-oriented dialogue agents to address user overreliance via friction turns.<n>Our empirical findings demonstrate that the proposed approach not only enables reliable estimation of AI agent errors but also guides the decoder in generating more accurate actions.
• Score: 9.305763502526833
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent LLMs have enabled significant advancements for conversational agents. However, they are also well known to hallucinate, producing responses that seem plausible but are factually incorrect. On the other hand, users tend to over-rely on LLM-based AI agents, accepting AI's suggestion even when it is wrong. Adding positive friction, such as explanations or getting user confirmations, has been proposed as a mitigation in AI-supported decision-making systems. In this paper, we propose an accountability model for LLM-based task-oriented dialogue agents to address user overreliance via friction turns in cases of model uncertainty and errors associated with dialogue state tracking (DST). The accountability model is an augmented LLM with an additional accountability head that functions as a binary classifier to predict the relevant slots of the dialogue state mentioned in the conversation. We perform our experiments with multiple backbone LLMs on two established benchmarks (MultiWOZ and Snips). Our empirical findings demonstrate that the proposed approach not only enables reliable estimation of AI agent errors but also guides the decoder in generating more accurate actions. We observe around 3% absolute improvement in joint goal accuracy (JGA) of DST output by incorporating accountability heads into modern LLMs. Self-correcting the detected errors further increases the JGA from 67.13 to 70.51, achieving state-of-the-art DST performance. Finally, we show that error correction through user confirmations (friction turn) achieves a similar performance gain, highlighting its potential to reduce user overreliance.

Related papers

• Enhancing LLM Reliability via Explicit Knowledge Boundary Modeling [48.15636223774418]
  Large language models (LLMs) frequently hallucinate due to misaligned self-awareness. Existing approaches mitigate hallucinations via uncertainty estimation or query rejection. We propose the Explicit Knowledge Boundary Modeling framework to integrate fast and slow reasoning systems.
  arXiv  Detail & Related papers  (2025-03-04T03:16:02Z)
• Aligning Large Language Models for Faithful Integrity Against Opposing Argument [71.33552795870544]
  Large Language Models (LLMs) have demonstrated impressive capabilities in complex reasoning tasks. They can be easily misled by unfaithful arguments during conversations, even when their original statements are correct. We propose a novel framework, named Alignment for Faithful Integrity with Confidence Estimation.
  arXiv  Detail & Related papers  (2025-01-02T16:38:21Z)
• LLM2: Let Large Language Models Harness System 2 Reasoning [65.89293674479907]
  Large language models (LLMs) have exhibited impressive capabilities across a myriad of tasks, yet they occasionally yield undesirable outputs. We introduce LLM2, a novel framework that combines an LLM with a process-based verifier. LLMs2 is responsible for generating plausible candidates, while the verifier provides timely process-based feedback to distinguish desirable and undesirable outputs.
  arXiv  Detail & Related papers  (2024-12-29T06:32:36Z)
• Understanding the Dark Side of LLMs' Intrinsic Self-Correction [55.51468462722138]
  Intrinsic self-correction was proposed to improve LLMs' responses via feedback prompts solely based on their inherent capability.<n>Recent works show that LLMs' intrinsic self-correction fails without oracle labels as feedback prompts.<n>We identify intrinsic self-correction can cause LLMs to waver both intermedia and final answers and lead to prompt bias on simple factual questions.
  arXiv  Detail & Related papers  (2024-12-19T15:39:31Z)
• Drawing the Line: Enhancing Trustworthiness of MLLMs Through the Power of Refusal [21.342265570934995]
  Existing methods have largely overlooked the importance of refusal responses as a means of enhancing MLLMs reliability.<n>We present the Information Boundary-aware Learning Framework (InBoL), a novel approach that empowers MLLMs to refuse to answer user queries when encountering insufficient information.<n>This framework introduces a comprehensive data generation pipeline and tailored training strategies to improve the model's ability to deliver appropriate refusal responses.
  arXiv  Detail & Related papers  (2024-12-15T14:17:14Z)
• Watson: A Cognitive Observability Framework for the Reasoning of LLM-Powered Agents [7.392058124132526]
  Foundations models (FMs) play an increasingly prominent role in complex software systems, such as agentic software. Fast-thinking Large Language Models (LLMs) are still preferred due to latency constraints. We introduce Watson, a framework that provides reasoning observability into implicit reasoning processes.
  arXiv  Detail & Related papers  (2024-11-05T19:13:22Z)
• Automatic Curriculum Expert Iteration for Reliable LLM Reasoning [60.60318625779015]
  Hallucinations (i.e., generating plausible but inaccurate content) and laziness (i.e. excessive refusals or defaulting to "I don't know") persist as major challenges in LLM reasoning. Current efforts to reduce hallucinations primarily focus on factual errors in knowledge-grounded tasks, often neglecting hallucinations related to faulty reasoning. We propose Automatic Curriculum Expert Iteration (Auto-CEI) to enhance LLM reasoning and align responses to the model's capabilities.
  arXiv  Detail & Related papers  (2024-10-10T05:43:07Z)
• Automated test generation to evaluate tool-augmented LLMs as conversational AI agents [0.27309692684728615]
  We present a test generation pipeline to evaluate conversational AI agents. Our framework uses LLMs to generate diverse tests grounded on user-defined procedures. Our results show that while tool-augmented LLMs perform well in single interactions, they often struggle to handle complete conversations.
  arXiv  Detail & Related papers  (2024-09-24T09:57:43Z)
• Evaluating Uncertainty-based Failure Detection for Closed-Loop LLM Planners [10.746821861109176]
  Large Language Models (LLMs) have witnessed remarkable performance as zero-shot task planners for robotic tasks. However, the open-loop nature of previous works makes LLM-based planning error-prone and fragile. In this work, we introduce a framework for closed-loop LLM-based planning called KnowLoop, backed by an uncertainty-based MLLMs failure detector.
  arXiv  Detail & Related papers  (2024-06-01T12:52:06Z)
• SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales [29.33581578047835]
  SaySelf is a training framework that teaches large language models to express more accurate fine-grained confidence estimates. In addition, SaySelf directs LLMs to produce self-reflective rationales that clearly identify gaps in their parametric knowledge. We show that the generated self-reflective rationales are reasonable and can further contribute to the calibration.
  arXiv  Detail & Related papers  (2024-05-31T16:21:16Z)
• Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration [70.09561665520043]
  We propose a novel framework for multi-agent collaboration that introduces Reinforced Advantage feedback (ReAd) for efficient self-refinement of plans. We provide theoretical analysis by extending advantage-weighted regression in reinforcement learning to multi-agent systems. Experiments on Over-AI and a difficult variant of RoCoBench show that ReAd surpasses baselines in success rate, and also significantly decreases the interaction steps of agents.
  arXiv  Detail & Related papers  (2024-05-23T08:33:19Z)
• "I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust [51.542856739181474]
  We show how different natural language expressions of uncertainty impact participants' reliance, trust, and overall task performance. We find that first-person expressions decrease participants' confidence in the system and tendency to agree with the system's answers, while increasing participants' accuracy. Our findings suggest that using natural language expressions of uncertainty may be an effective approach for reducing overreliance on LLMs, but that the precise language used matters.
  arXiv  Detail & Related papers  (2024-05-01T16:43:55Z)
• Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection [90.71323430635593]
  We propose a novel self-detection paradigm that considers the comprehensive answer space beyond LLM-generated answers. Building upon this paradigm, we introduce a two-step framework, which firstly instructs LLM to reflect and provide justifications for each candidate answer. This framework can be seamlessly integrated with existing approaches for superior self-detection.
  arXiv  Detail & Related papers  (2024-03-15T02:38:26Z)
• Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models [84.94220787791389]
  We propose Fact-and-Reflection (FaR) prompting, which improves the LLM calibration in two steps. Experiments show that FaR achieves significantly better calibration; it lowers the Expected Error by 23.5%. FaR even elicits the capability of verbally expressing concerns in less confident scenarios.
  arXiv  Detail & Related papers  (2024-02-27T01:37:23Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)
• Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness [16.35655151252159]
  We introduce BSDetector, a method for detecting bad and speculative answers from a pretrained Large Language Model. Our uncertainty quantification technique works for any LLM accessible only via a black-box API.
  arXiv  Detail & Related papers  (2023-08-30T17:53:25Z)

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.