aiqbench analysis
This research introduces AttentionPO, a method that leverages a model's internal attention mechanisms to dynamically weight tokens during Direct Preference Optimization, addressing the inefficiency of treating all tokens equally. By using the LLM itself as a content-aware judge, this approach eliminates the need for separate reward models or heuristic position-based weights while significantly boosting performance on standard alignment benchmarks. The technique offers a more robust and cost-effective path for refining Large Language Models without incurring extra training overhead.
Token-weighted Direct Preference Optimization with Attention
arXiv:2605.21883v3 Announce Type: replace Abstract: Direct Preference Optimization (DPO) aligns Large Language Models with human preferences without the need for a separate reward model. However, DPO treats all tokens in responses equally, neglecting the differing importance of individual tokens. Existing token-level PO methods compute the token weights using either token-position-based heuristic functions or probability estimates given by a separately trained model, which lacks robustness and incurs extra training cost. In contrast, we propose Token-weighted DPO (TwDPO) -- a novel training objective grounded on token-weighted RL -- and AttentionPO -- an instantiation of TwDPO that uses attention from the LLM itself to estimate token weights. AttentionPO prompts the LLM to serve as a pairwise judge and check where the model attends when comparing the responses. This design makes AttentionPO content-aware, adjusting weights based on response content, and efficient, incurring only two extra forward passes per example. Experiment results show that AttentionPO significantly improves performance on AlpacaEval, MT-Bench, and ArenaHard, surpassing existing Preference Optimization methods.
Abstract:Direct Preference Optimization (DPO) aligns Large Language Models with human preferences without the need for a separate reward model. However, DPO treats all tokens in responses equally, neglecting the differing importance of individual tokens. Existing token-level PO methods compute the token weights using either token-position-based heuristic functions or probability estimates given by a separately trained model, which lacks robustness and incurs extra training cost. In contrast, we propose Token-weighted DPO (TwDPO) -- a novel training objective grounded on token-weighted RL -- and AttentionPO -- an instantiation of TwDPO that uses attention from the LLM itself to estimate token weights. AttentionPO prompts the LLM to serve as a pairwise judge and check where the model attends when comparing the responses. This design makes AttentionPO content-aware, adjusting weights based on response content, and efficient, incurring only two extra forward passes per example. Experiment results show that AttentionPO significantly improves performance on AlpacaEval, MT-Bench, and ArenaHard, surpassing existing Preference Optimization methods.
Computation and Language (cs.CL)
arXiv:2605.21883 [cs.CL]
(or arXiv:2605.21883v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.21883 Focus to learn more arXiv-issued DOI via DataCite
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