Teaching Language Models to Hallucinate Less with Synthetic Tasks

• URL: http://arxiv.org/abs/2310.06827v3
• Date: Tue, 7 Nov 2023 05:11:46 GMT
• Title: Teaching Language Models to Hallucinate Less with Synthetic Tasks
• Authors: Erik Jones, Hamid Palangi, Clarisse Sim\~oes, Varun Chandrasekaran, Subhabrata Mukherjee, Arindam Mitra, Ahmed Awadallah, Ece Kamar
• Abstract summary: Large language models (LLMs) frequently hallucinate on abstractive summarization tasks. We show that reducing hallucination on a synthetic task can also reduce hallucination on real-world downstream tasks.
• Score: 47.87453655902263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) frequently hallucinate on abstractive summarization tasks such as document-based question-answering, meeting summarization, and clinical report generation, even though all necessary information is included in context. However, optimizing LLMs to hallucinate less on these tasks is challenging, as hallucination is hard to efficiently evaluate at each optimization step. In this work, we show that reducing hallucination on a synthetic task can also reduce hallucination on real-world downstream tasks. Our method, SynTra, first designs a synthetic task where hallucinations are easy to elicit and measure. It next optimizes the LLM's system message via prefix-tuning on the synthetic task, and finally transfers the system message to realistic, hard-to-optimize tasks. Across three realistic abstractive summarization tasks, SynTra reduces hallucination for two 13B-parameter LLMs using only a synthetic retrieval task for supervision. We also find that optimizing the system message rather than the model weights can be critical; fine-tuning the entire model on the synthetic task can counterintuitively increase hallucination. Overall, SynTra demonstrates that the extra flexibility of working with synthetic data can help mitigate undesired behaviors in practice.

Related papers

• Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models [0.0]
  Large Language Models (LLMs) are powerful computational models trained on extensive corpora of human-readable text, enabling them to perform general-purpose language understanding and generation. Despite these successes, LLMs often produce inaccuracies, commonly referred to as hallucinations. This paper provides an empirical evaluation of different prompting strategies and frameworks aimed at reducing hallucinations in LLMs.
  arXiv  Detail & Related papers  (2024-10-25T08:34:53Z)
• FG-PRM: Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning [10.709365940160685]
  Existing approaches primarily detect the presence of hallucinations but lack a nuanced understanding of their types and manifestations. We introduce a comprehensive taxonomy that categorizes the common hallucinations in mathematical reasoning task into six types. We then propose FG-PRM, an augmented model designed to detect and mitigate hallucinations in a fine-grained, step-level manner.
  arXiv  Detail & Related papers  (2024-10-08T19:25:26Z)
• ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models [65.12177400764506]
  Large language models (LLMs) exhibit hallucinations in long-form question-answering tasks across various domains and wide applications. Current hallucination detection and mitigation datasets are limited in domains and sizes. This paper introduces an iterative self-training framework that simultaneously and progressively scales up the hallucination annotation dataset.
  arXiv  Detail & Related papers  (2024-07-05T17:56:38Z)
• Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization [123.54980913741828]
  Large Visual Language Models (LVLMs) have demonstrated exceptional abilities in understanding multimodal data. They invariably suffer from hallucinations, leading to a disconnect between the generated text and the corresponding images. Almost all current visual contrastive decoding methods attempt to mitigate these hallucinations by introducing visual uncertainty information. However, they struggle to precisely induce the hallucinatory tokens, which severely limits their effectiveness in mitigating hallucinations.
  arXiv  Detail & Related papers  (2024-05-24T08:46:31Z)
• Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback [48.065569871444275]
  We propose detecting and mitigating hallucinations in Large Vision Language Models (LVLMs) via fine-grained AI feedback. We generate a small-size hallucination annotation dataset by proprietary models. Then, we propose a detect-then-rewrite pipeline to automatically construct preference dataset for training hallucination mitigating model.
  arXiv  Detail & Related papers  (2024-04-22T14:46:10Z)
• Don't Believe Everything You Read: Enhancing Summarization Interpretability through Automatic Identification of Hallucinations in Large Language Models [0.0]
  This paper takes a deep dive into Large Language Models (LLMs) behavior with respect to hallucinations. It defines a token-level approach to identifying different kinds of hallucinations, and further utilizes this token-level tagging to improve the interpretability and faithfulness of LLMs.
  arXiv  Detail & Related papers  (2023-12-22T00:31:46Z)
• ORES: Open-vocabulary Responsible Visual Synthesis [104.7572323359984]
  We formalize a new task, Open-vocabulary Responsible Visual Synthesis (ORES), where the synthesis model is able to avoid forbidden visual concepts. To address this problem, we present a Two-stage Intervention (TIN) framework. By introducing 1) rewriting with learnable instruction through a large-scale language model (LLM) and 2) synthesizing with prompt intervention on a diffusion model, it can effectively synthesize images avoiding any concepts but following the user's query as much as possible.
  arXiv  Detail & Related papers  (2023-08-26T06:47:34Z)
• ContraNeRF: Generalizable Neural Radiance Fields for Synthetic-to-real Novel View Synthesis via Contrastive Learning [102.46382882098847]
  We first investigate the effects of synthetic data in synthetic-to-real novel view synthesis. We propose to introduce geometry-aware contrastive learning to learn multi-view consistent features with geometric constraints. Our method can render images with higher quality and better fine-grained details, outperforming existing generalizable novel view synthesis methods in terms of PSNR, SSIM, and LPIPS.
  arXiv  Detail & Related papers  (2023-03-20T12:06:14Z)

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.