MAKING LARGE LANGUAGE MODELS BETTER REA-SONERS WITH ALIGNMENT
This paper discusses how to improve the reasoning abilities of AI language models, which are crucial for creating smarter AI systems. It identifies a problem where these models sometimes give better scores to incorrect answers and proposes a new training method to.
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- 1 Studies train Large Language Models (LLMs) using maximum likelihood estimation (MLE) and a next-token prediction objective.
- 2 Pre-training methods pre-train Large Language Models (LLMs) on unsupervised datasets with a next token prediction objective.
- 3 The maximum likelihood estimation (MLE) objective allocates probability mass exclusively to the reference chain of thought (COT).
- 4 The objective treats all other correct and incorrect chain of thoughts (COTs) as negative examples.
Introduction
Researchers focus on enhancing the reasoning abilities of open-source Large Language Models (LLMs) because they lack skills essential for artificial general intelligence agents. Training Large Language Models (LLMs) with chain of thought (COT) data effectively improves their reasoning ability.
Maximum likelihood estimation (MLE) contradicts reasoning tasks by assigning probability mass only to the reference chain of thought (COT).
The vanilla fine-tuning (VFT) paradigm causes Large Language Models (LLMs) to suffer from an Assessment Misalignment problem.
VFT-LLMs cannot accurately discern the quality of various chain of thoughts (COTs).
A small beta cannot effectively widen the score gap between high-quality and low-quality chain of thoughts (COTs).
Research Question
Studies train Large Language Models (LLMs) using maximum likelihood estimation (MLE) and a next-token prediction objective. Pre-training methods pre-train Large Language Models (LLMs) on unsupervised datasets with a next token prediction objective.
The maximum likelihood estimation (MLE) objective allocates probability mass exclusively to the reference chain of thought (COT).
The objective treats all other correct and incorrect chain of thoughts (COTs) as negative examples.
Methodology
Alignment Fine-Tuning (AFT) performs well in multi-task and out-of-distribution situations. Table 1 shows the task accuracy and assessment accuracy of different vanilla fine-tuned models.
Study Design
Task accuracy and assessment accuracy exhibit a strong positive correlation on GSM8K and ECQA.
The method elevates the negative score when it is below the boundary.
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Results & Findings
Pilot experiments find that Large Language Models (LLMs) with better reasoning performance give more reasonable assessments to different chain of thoughts (COTs) after vanilla fine-tuning (VFT). We propose an alignment fine-tuning (AFT) paradigm with three steps to improve Large Language Model (LLM) reasoning.
- Pilot experiments find that Large Language Models (LLMs) with better reasoning performance give more reasonable assessments to different chain of thoughts (COTs) after vanilla fine-tuning (VFT).
- We propose an alignment fine-tuning (AFT) paradigm with three steps to improve Large Language Model (LLM) reasoning.
- A constraint term protects negative scores to prevent model degradation.
- We find that the constraint overlooked by recent ranking-based alignment methods like DPO, PRO, and RRHF is crucial for their effectiveness.
- We present an Alignment Fine-Tuning (AFT) paradigm with a novel constraint alignment loss to address the Assessment Misalignment problem.
We create GSM8K-RANK to evaluate the effectiveness of AFT in the ranking situation.
We conduct a case study in Appendix E to show model degradation without constraint.
Practical Applications
At the end of the school year, Kate asked her teacher if she could have the 3 boxes of 64 crayons since they were all worn down to small pieces.
Alignment Fine-Tuning (AFT) Paradigm
The AFT paradigm consists of three steps aimed at improving LLM reasoning by fine-tuning with COT data, generating multiple COT responses, and calibrating scores using a constraint alignment loss.
Assessment Misalignment Problem
The paper identifies the Assessment Misalignment problem where LLMs assign higher scores to incorrect reasoning paths, limiting their reasoning capabilities.
Figures Explained
Frequently Asked Questions
Studies train Large Language Models (LLMs) using maximum likelihood estimation (MLE) and a next-token prediction objective. Pre-training methods pre-train Large Language Models (LLMs) on unsupervised datasets with a next token prediction objective.
Table 1 shows the task accuracy and assessment accuracy of different vanilla fine-tuned models. The method elevates the negative score when it is below the boundary.
We create GSM8K-RANK to evaluate the effectiveness of AFT in the ranking situation. We conduct a case study in Appendix E to show model degradation without constraint.
Reasoning utilizes evidence to reach a well-founded conclusion. At the end of the school year, Kate asked her teacher if she could have the 3 boxes of 64 crayons since they were all worn down to small pieces.
VFT-LLMs cannot accurately discern the quality of various chain of thoughts (COTs). A small beta cannot effectively widen the score gap between high-quality and low-quality chain of thoughts (COTs).
This paper discusses how to improve the reasoning abilities of AI language models, which are crucial for creating smarter AI systems. It identifies a problem where these models sometimes give better scores to incorrect answers and proposes a new training method to.