Would I Lie To You? Inference Time Alignment of Language Models using Direct Preference Heads

TL;DR

This paper introduces Direct Preference Heads, a fine-tuning method that aligns language models with human preferences without compromising their reasoning, demonstrated through improved performance on multiple benchmarks.

Contribution

The paper proposes a novel fine-tuning framework using auxiliary reward heads to better align language models with human preferences while preserving reasoning capabilities.

Findings

Models with DPH outperform SFT and DPO on GLUE, RACE, and GPT4All.

Theoretical analysis links DPH to Conservative Direct Preference Optimization.

DPH maintains reasoning abilities while improving alignment with human preferences.

Abstract

Pre-trained Language Models (LMs) exhibit strong zero-shot and in-context learning capabilities; however, their behaviors are often difficult to control. By utilizing Reinforcement Learning from Human Feedback (RLHF), it is possible to fine-tune unsupervised LMs to follow instructions and produce outputs that reflect human preferences. Despite its benefits, RLHF has been shown to potentially harm a language model's reasoning capabilities and introduce artifacts such as hallucinations where the model may fabricate facts. To address this issue we introduce Direct Preference Heads (DPH), a fine-tuning framework that enables LMs to learn human preference signals through an auxiliary reward head without directly affecting the output distribution of the language modeling head. We perform a theoretical analysis of our objective function and find strong ties to Conservative Direct Preference Optimization (cDPO). Finally we evaluate our models on GLUE, RACE, and the GPT4All evaluation suite and demonstrate that our method produces models which achieve higher scores than those fine-tuned with Supervised Fine-Tuning (SFT) or Direct Preference Optimization (DPO) alone.