Pretrained Transformer Language Models for Search: Can LLMs Reason and Plan?

Pretrained Transformer Language Models for Search: Can LLMs Reason and Plan?

In the world of natural language processing, pretrained transformer language models (LLMs) have gained significant popularity for their ability to generate coherent and contextually relevant text. These models, such as GPT-3 and GPT-4, have shown remarkable performance in various language tasks, including search and retrieval. However, there are ongoing debates about whether LLMs possess true reasoning and planning capabilities.

One example of a popular lexical-based retrieval scoring function is BM25. This function allows for efficient retrieval of relevant documents by utilizing inverted indexes and dynamic pruning algorithms like WAND. By avoiding exhaustive scoring of all matching documents, retrieval time can be significantly reduced. Additionally, dense retrieval can be accelerated by employing approximate nearest neighbor search techniques, such as indexing document vector representations using HNSW graph indexing.

While LLMs excel in idea generation for a wide range of tasks, including those involving reasoning, it is important to note that their ability to generate potential solutions does not guarantee autonomous reasoning capabilities. However, this does not diminish the value LLMs can bring to planning and reasoning tasks. In what is referred to as "LLM-Modulo" setups, LLMs can be effectively utilized in conjunction with model-based planners, external solvers, or expert humans in the loop.

One approach to test the planning abilities of LLMs is to reduce the effectiveness of approximate retrieval by obfuscating the names of actions and objects in planning problems. Surprisingly, when this was done for test domains, the empirical performance of GPT-4, a highly advanced LLM, dropped significantly. This suggests that the improved performance of GPT-4 may not solely be attributed to its planning abilities. In fact, standard AI planners had no trouble dealing with the obfuscation.

To ensure the correctness of solutions generated by LLMs, it is advisable to include an external model-based plan verifier in the process. This verifier can provide back prompting and certify the accuracy of the final solution. However, it is important to be cautious of the Clever Hans effect, where the LLM is merely generating guesses and the human in the loop unintentionally directs the LLM towards the correct solution. This effect can compromise the true reasoning capabilities of LLMs.

In conclusion, while LLMs may not possess inherent planning and reasoning capabilities, their ability to generate potential solutions can be leveraged effectively in combination with external solvers, expert humans, or model-based planners. To ensure the accuracy of solutions, it is crucial to incorporate external verification mechanisms. Here are three actionable pieces of advice when working with LLMs in planning and reasoning tasks:

1. Use LLMs in conjunction with external solvers: By combining the idea generation capabilities of LLMs with the problem-solving abilities of external solvers, you can achieve more accurate and reliable results.

2. Incorporate model-based plan verification: Include an external model-based plan verifier to certify the correctness of solutions generated by LLMs. This will help eliminate the Clever Hans effect and ensure the true reasoning capabilities of LLMs are utilized.

3. Avoid overestimating LLMs' autonomous reasoning abilities: While LLMs can generate potential solutions, it is important to recognize their limitations and not ascribe autonomous reasoning capabilities to them. Use LLMs as a valuable tool in the planning and reasoning process, but always verify and refine their output.

By following these recommendations, you can effectively leverage LLMs in planning and reasoning tasks while ensuring the accuracy and reliability of the solutions they generate. While LLMs may not be autonomous reasoners, their ability to generate ideas and potential solutions remains a valuable asset in problem-solving.