The Reversal Curse in Language Models
Despite their advanced reasoning abilities, the latest large language models (LLMs) often struggle to understand relationships effectively. This article discusses the “Reversal Curse,” a challenge that these models face in tasks like comprehension and generation.
Understanding the Reversal Curse
The Reversal Curse occurs when LLMs deal with two entities, a and b, connected by a relationship R and its inverse. While LLMs can easily handle sequences like “aRb,” they struggle with “b R inverse a.” For example, they can answer “Who is the mother of Tom Cruise?” but may falter when asked, “Who is Mary Lee Pfeiffer’s son?” This highlights a gap in their relational understanding.
Research Insights
Researchers from Renmin University of China have explored the Reversal Curse and its causes. They point out that the Training Objective Function plays a significant role in this issue.
Training Process of LLMs
The training process for LLMs typically involves next-token prediction (NTP). In this method, each token focuses only on the previous context, which limits the model’s ability to understand relationships involving inverse connections. This is why LLMs struggle with questions that require them to reverse the relationship.
Improving Performance with GLMs
In contrast, General Language Models (GLMs) use a different training approach that allows them to handle both preceding and succeeding tokens more effectively. This makes GLMs more robust against the Reversal Curse. For instance, when tested on a task involving names and descriptions, GLMs achieved about 80% accuracy, while Llama scored 0%.
Proposed Solutions
The researchers suggest adapting LLM training to be more like GLMs. They introduced Bidirectional Causal Language Model Optimization (BICO), which modifies the training objective to improve accuracy in tasks like reverse translation and mathematical problem-solving. This method incorporates bidirectional attention and rotary position embeddings.
Conclusion and Future Work
The study highlights the Reversal Curse and offers a fine-tuning strategy to overcome it. By using a causal language model with an ABI-like objective, the research opens doors for further exploration of advanced techniques, such as Reinforcement Learning from Human Feedback (RLHF).
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