Search engines don't always help chatbots generate accurate answers
"Our model learns to find helpful information from noisy search results and generate as accurate responses as possible. As a result, our model can better summarize valuable information and generate better answers for various search queries, even when search engines cannot handle them very well," Luo said.
"Our training explicitly includes a step that clarifies if each search result is helpful or not, and the language model follows the selected helpful information. This process filters out most unreliable and unrelated search results and improves the average instruction-following performance." Initial experiments showed that SAIL-7B outperformed GPT-3.5 and other models containing more parameters at a range of tasks. The experiments assessed their abilities to answer common sense and open-ended questions, as well as fact checking, and detecting hate speech. The models were fed web pages from Wikipedia and search results from DuckDuckGo to help them pick the right answers from a list of candidate responses. GPT-4, however, was still better than SAIL-7B.
"The challenge is that larger models have much stronger knowledge, memorizing and reasoning abilities, so our model is not as good as GPT-4 yet. However, SAIL-7B is a proof of concept with a 'small' model, and our next step is training a larger model with the strategy we have proposed," Luo told us. Models fine-tuned with the current search-augmented instruction learning technique aren't perfect, however. The researchers noted that they cannot explaina search result is trustworthy or not. They hope to come up with another strategy to increase accuracy and reliability in the future. ®
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