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HonestLLM: Toward an Honest and Helpful Large Language Model
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Proposes principles for honest LLMs, a 930-query HoneSet benchmark across six categories, and two methods (curiosity-driven prompting and two-stage DPO fine-tuning) to improve honesty and helpfulness.
Problem
LLMs often respond dishonestly (hallucinating answers to queries they cannot answer, such as real-time data, non-text modalities, or flawed premises) or honestly but unhelpfully; the paper asks whether helpfulness can be prioritized while preserving honesty.
Approach
Defines six categories of LLM-unable queries and a refined honesty definition; uses curiosity-driven prompting to elicit expressed confusion, then a constitution-guided rewrite for a training-free gain, and a curriculum-style two-stage DPO (first separate honest from dishonest, then improve helpfulness among honest responses).
Key results
- Training-free curiosity-driven method raised honesty rates for all nine evaluated models above 60%, e.g. Llama2-7b from 0.430 to 0.837 and GPT-4 and Claude3-Opus to 100%.
- Two-stage DPO on Llama3-8b raised honesty rate from 49.2% to 91.7% and H2 score from 4.975 to 8.225 (65.3% gain); on Mistral-7b, honesty rose from 32.5% to 85.8% (124.7% H2 gain).
- GPT-4 judge agreed with human annotators 91.43% of the time on 883 raw-vs-optimized pairs.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply curiosity-driven prompting and answer optimization | LLMs evaluated on HoneSet · HoneSet | Honesty Rate: 0.606 to 0.871 (Llama3-70b); 0.585 to 0.914 (Mixtral-8x7b); 0.430 to 0.837 (Llama2-7b) | Helped |
| Apply curiosity-driven prompting and answer optimization | LLMs evaluated on HoneSet · HoneSet | H² pairwise preference: 40.1% | Helped |
| Add two-stage fine-tuning | Llama3-8b · HoneSet | Honesty Rate: 49.2% -> 91.7% | Helped |
| Add two-stage fine-tuning | Llama3-8b · HoneSet | H² Score: 4.975 -> 8.225 | Helped |
| Add two-stage fine-tuning | Mistral-7b · HoneSet | Honesty Rate: 32.5% -> 85.8% | Helped |
| Add two-stage fine-tuning | Mistral-7b · HoneSet | H² Score: 3.308 -> 7.433 | Helped |
| Add stage two after stage one rather than use direct fine-tuning | Llama3-8b · HoneSet | H² Score: 6.575 -> 8.225 | Helped |
| Add stage two after stage one rather than use direct fine-tuning | Mistral-7b · HoneSet | H² Score: 6.733 -> 7.433 | Helped |
| Add stage two after stage one rather than use stage one only (ablation) | Llama3-8b · HoneSet | H² Score: 5.517 -> 8.225 | Helped |
| Add stage two after stage one rather than use stage one only (ablation) | Mistral-7b · HoneSet | H² Score: 4.642 -> 7.433 | Helped |
| Fine-tune the model | Fine-tuned model · MMLU | Accuracy: 51.4 -> 51.8 | Helped |
| Fine-tune the model | Fine-tuned model · MT-Bench | Score: 7.7 -> 7.3 | Hurt |
Surprising findings
- Direct fine-tuning on combined data reached higher honesty rate (82.5%) than stage-1 alone on Llama3-8b, but the two-stage approach gave the best H2 score.
- Fine-tuning slightly reduced MT-Bench score (7.7 to 7.3) while MMLU rose slightly (51.4 to 51.8).
Limitations
- Honesty and H2 evaluation depend on GPT-4 as judge, with only 91.43% agreement with humans on a subset.
- Fine-tuning reduced MT-Bench score by about 5%, indicating a trade-off with general capability.
- Fine-tuning experiments limited to two 7-8B models and 120 test queries.
Experimental setup
Datasets: HoneSet MMLU MT-Bench TrustLLM safety subset
Benchmarks: HoneSet MMLU MT-Bench TrustLLM safety subset
Metrics: honesty rate H2 score pairwise win rate refusal rate accuracy MT-Bench score
Baselines: ChatGPT GPT-4 Claude3-Opus Llama2-7b Llama2-13b Llama2-70b Llama3-70b Mistral-7b Mixtral-8x7b Direct fine-tuning
Builds on: DPO LoRA curriculum learning self-alignment TrustLLM
Closest papers by meaning
- Alignment for Honesty (NeurIPS 2024 · similarity 0.92)
- MoHoBench: Assessing Honesty of Multimodal Large Language Models via Unanswerable Visual Questions (AAAI 2026 · similarity 0.90)
- To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contexts (ICLR 2025 · similarity 0.90)
- Aligning Large Language Models for Faithful Integrity Against Opposing Argument (AAAI 2025 · similarity 0.90)
- Position: TrustLLM: Trustworthiness in Large Language Models (ICML 2024 · similarity 0.89)
- How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? (ICML 2024 · similarity 0.89)
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