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Data Center News > Blog > AI > Bridging the gap between LLMs and symbolic reasoning
AI

Bridging the gap between LLMs and symbolic reasoning

Last updated: June 14, 2024 7:54 pm
Published June 14, 2024
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Bridging the gap between LLMs and symbolic reasoning
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Researchers have introduced a novel strategy referred to as pure language embedded packages (NLEPs) to enhance the numerical and symbolic reasoning capabilities of enormous language fashions (LLMs). The approach entails prompting LLMs to generate and execute Python packages to unravel consumer queries, then output options in pure language.

Whereas LLMs like ChatGPT have demonstrated spectacular efficiency on varied duties, they typically battle with issues requiring numerical or symbolic reasoning.

NLEPs observe a four-step problem-solving template: calling vital packages, importing pure language representations of required data, implementing a solution-calculating operate, and outputting outcomes as pure language with non-compulsory information visualisation.

This strategy affords a number of benefits, together with improved accuracy, transparency, and effectivity. Customers can examine generated packages and repair errors instantly, avoiding the necessity to rerun complete fashions for troubleshooting. Moreover, a single NLEP could be reused for a number of duties by changing sure variables.

The researchers discovered that NLEPs enabled GPT-4 to realize over 90% accuracy on varied symbolic reasoning duties, outperforming task-specific prompting strategies by 30%

Past accuracy enhancements, NLEPs might improve information privateness by working packages domestically, eliminating the necessity to ship delicate consumer information to exterior firms for processing. The approach may additionally enhance the efficiency of smaller language fashions with out expensive retraining.

Nevertheless, NLEPs depend on a mannequin’s program era functionality and will not work as effectively with smaller fashions educated on restricted datasets. Future analysis will discover strategies to make smaller LLMs generate simpler NLEPs and examine the impression of immediate variations on reasoning robustness.

The analysis, supported partly by the Middle for Perceptual and Interactive Intelligence of Hong Kong, can be offered on the Annual Conference of the North American Chapter of the Association for Computational Linguistics later this month.

See also  Salesforce launches Agentforce 3 with AI agent observability and MCP support

(Photograph by Alex Azabache)

See additionally: Apple is reportedly getting free ChatGPT entry

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Tags: ai, synthetic intelligence, improvement, massive language fashions, llm, pure language, nlep

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TAGGED: Bridging, gap, LLMs, reasoning, symbolic
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