mlsanityData to 7 Oct 2026

Truth-Aware Context Selection: Mitigating Hallucinations of Large Language Models Being Misled by Untruthful Contexts

arXiv 2403.07556 ↗First posted 12 Mar 2024cs.CLcited by 15

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Abstract (arXiv)

Although Large Language Models (LLMs) have demonstrated impressive text generation capabilities, they are easily misled by untruthful contexts provided by users or knowledge augmentation tools, leading to hallucinations. To alleviate LLMs from being misled by untruthful context and take advantage of knowledge augmentation, we propose Truth-Aware Context Selection (TACS), a lightweight method to adaptively recognize and mask untruthful context from the inputs. TACS begins by performing truth detection on the input context, leveraging the parameterized knowledge within the LLM. Subsequently, it constructs a corresponding attention mask based on the truthfulness of each position, selecting the truthful context and discarding the untruthful context. Additionally, we introduce a new evaluation metric, Disturbance Adaption Rate, to further study the LLMs' ability to accept truthful information and resist untruthful information. Experimental results indicate that TACS can effectively filter untruthful context and significantly improve the overall quality of LLMs' responses when presented with misleading information.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 27 of its 39 references that are in the map. First posted on arXiv 12 Mar 2024.

Builds on

27 of 39 references.

References · 27

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  5. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  6. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  7. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,259
  8. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,239
  9. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  10. 2023LIMA: Less Is More for Alignmentcited by 1,080
  11. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
  12. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
15 more
  1. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  2. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  3. 2023Bias and Fairness in Large Language Models: A Surveycited by 626
  4. 2023Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflictscited by 333
  5. 2023A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generationcited by 202
  6. 2022Factuality Enhanced Language Models for Open-Ended Text Generationcited by 200
  7. 2023LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examplescited by 177
  8. 2023Sources of Hallucination by Large Language Models on Inference Taskscited by 168
  9. 2023Med-HALT: Medical Domain Hallucination Test for Large Language Modelscited by 156
  10. 2023Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answeringcited by 150
  11. 2023Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentationcited by 146
  12. 2022On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?cited by 137
  13. 2024TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Spacecited by 90
  14. 2023Auto-Regressive Next-Token Predictors are Universal Learnerscited by 65
  15. 2024SiLLM: Large Language Models for Simultaneous Machine Translationcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 9; 2026: 3.

15 papers.

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  1. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 125
  2. 2024Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Modelscited by 41
  3. 2024To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contextscited by 25
  4. 2024Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contextscited by 19
  5. 2025Attention Hijackers: Detect and Disentangle Attention Hijacking in LVLMs for Hallucination Mitigationcited by 9
  6. 2025AlignX: Advancing Multilingual Large Language Models with Multilingual Representation Alignmentcited by 6
  7. 2025Accommodate Knowledge Conflicts in Retrieval-augmented LLMs: Towards Robust Response Generation in the Wildcited by 4
  8. 2025From Misleading Queries to Accurate Answers: A Three-Stage Fine-Tuning Method for LLMscited by 2
  9. 2025IG-Pruning: Input-Guided Block Pruning for Large Language Modelscited by 2
  10. 2025Collaborative QA using Interacting LLMs. Impact of Network Structure, Node Capability and Distributed Datacited by 2
  11. 2025AIstorian lets AI be a historian: A KG-powered multi-agent system for accurate biography generationcited by 1
  12. 2025DePass: Unified Feature Attributing by Simple Decomposed Forward Passcited by 1
3 more
  1. 2026Trust or Abstain? A Self-Aware RAG Approachcited by 1
  2. 2026Diagnosing LLM Arbitration Behavior over Pre-evidence Epistemic States in RAG-based Fact-Checkingcited by 1
  3. 2026Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarizationcited by 0