{"arxiv_id":"2407.01046","incoming":{"papers":0,"sentences":0,"groups":[],"unlabelled_sentences":0},"outgoing":{"papers":34,"sentences":41,"groups":[{"kind":"method_or_tool","label":"Uses the method or tool of","papers":5,"sentences":5,"quotes":[{"arxiv_id":"2308.09583","title":"WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct","url":"/paper/2308.09583","section":"Experiment","sentence":"We evaluated several open-sourced LLMs, including Llama-2 [ref] , CodeLlama [ref] , Llemma [ref] , llama-3 [ref] , Mistral [ref] , Qwen-1.5 [ref] , Tulu-2 [ref] , WizardLM [ref] , WizardMath [ref] and Yi-Chat [ref] on our benchmark.","intent_p":0.81,"contests_p":null},{"arxiv_id":"2308.12950","title":"Code Llama: Open Foundation Models for Code","url":"/paper/2308.12950","section":"Experiment","sentence":"We evaluated several open-sourced LLMs, including Llama-2 [ref] , CodeLlama [ref] , Llemma [ref] , llama-3 [ref] , Mistral [ref] , Qwen-1.5 [ref] , Tulu-2 [ref] , WizardLM [ref] , WizardMath [ref] and Yi-Chat [ref] on our benchmark.","intent_p":0.81,"contests_p":null},{"arxiv_id":"2310.06825","title":"Mistral 7B","url":"/paper/2310.06825","section":"Experiment","sentence":"We evaluated several open-sourced LLMs, including Llama-2 [ref] , CodeLlama [ref] , Llemma [ref] , llama-3 [ref] , Mistral [ref] , Qwen-1.5 [ref] , Tulu-2 [ref] , WizardLM [ref] , WizardMath [ref] and Yi-Chat [ref] on our benchmark.","intent_p":0.81,"contests_p":null},{"arxiv_id":"2311.10702","title":"Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2","url":"/paper/2311.10702","section":"Experiment","sentence":"We evaluated several open-sourced LLMs, including Llama-2 [ref] , CodeLlama [ref] , Llemma [ref] , llama-3 [ref] , Mistral [ref] , Qwen-1.5 [ref] , Tulu-2 [ref] , WizardLM [ref] , WizardMath [ref] and Yi-Chat [ref] on our benchmark.","intent_p":0.81,"contests_p":null},{"arxiv_id":"2403.04652","title":"Yi: Open Foundation Models by 01.AI","url":"/paper/2403.04652","section":"Experiment","sentence":"We evaluated several open-sourced LLMs, including Llama-2 [ref] , CodeLlama [ref] , Llemma [ref] , llama-3 [ref] , Mistral [ref] , Qwen-1.5 [ref] , Tulu-2 [ref] , WizardLM [ref] , WizardMath [ref] and Yi-Chat [ref] on our benchmark.","intent_p":0.81,"contests_p":null}]},{"kind":"data_used","label":"Uses data or values from","papers":1,"sentences":1,"quotes":[{"arxiv_id":"2110.14168","title":"Training Verifiers to Solve Math Word Problems","url":"/paper/2110.14168","section":"Introduction","sentence":"To this end, we have developed , a benchmark for Fuzzy Reasoning benchmark of Generalized quantifiers, which transforms real-world mathematical problems from GSM8K [ref] and MathQA [ref] datasets into multiple-choice questions.","intent_p":0.93,"contests_p":null}]},{"kind":"background","label":"Cites as background","papers":5,"sentences":5,"quotes":[{"arxiv_id":"2001.08361","title":"Scaling Laws for Neural Language Models","url":"/paper/2001.08361","section":"Q2: Can the scaling law be observed in ?","sentence":"Scaling law is introduced in [ref] to suggest the phenomenon that LLMs can achieve enhanced task performance by scaling up model sizes.","intent_p":1.0,"contests_p":null},{"arxiv_id":"2205.15016","title":"A Fundamental Probabilistic Fuzzy Logic Framework Suitable for Causal Reasoning","url":"/paper/2205.15016","section":"Introduction","sentence":"Existing works like [ref] investigate employing fuzzy probabilistic theory in association-based problems, like the relation between fuzzy features and labels (e.g. a lot of smoking and cancer).","intent_p":0.84,"contests_p":null},{"arxiv_id":"2304.09797","title":"Progressive-Hint Prompting Improves Reasoning in Large Language Models","url":"/paper/2304.09797","section":"Related Work","sentence":"Various approaches have been developed to enhance the math reasoning ability of LLMs, like prompt-based designs [ref] .","intent_p":0.85,"contests_p":null},{"arxiv_id":"2308.00304","title":"Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models","url":"/paper/2308.00304","section":"Related Work","sentence":"Various approaches have been developed to enhance the math reasoning ability of LLMs, like prompt-based designs [ref] .","intent_p":0.85,"contests_p":null},{"arxiv_id":"2311.09277","title":"Contrastive Chain-of-Thought Prompting","url":"/paper/2311.09277","section":"Related Work","sentence":"Various approaches have been developed to enhance the math reasoning ability of LLMs, like prompt-based designs [ref] .","intent_p":0.85,"contests_p":null}]}],"unlabelled_sentences":30},"claims":[],"thresholds":{"intent_p":0.8,"supports_p":0.95,"contests_p":0.7},"method":"Citing sentences come from each paper's LaTeX source; every \\cite marks the sentence and the works it cites. Pith's classifier typed each sentence. A label is shown at intent probability 0.8 or more (0.95 for support), a contest at 0.7 or more. Measured accuracy against two blind model families is at https://pith.science/accuracy. The quoted sentences are the citing authors' own.","held_up":null,"_links":{"html":"https://pith.science/paper/2407.01046"}}