Title: Sample, Align, Synthesize: Graph-Based Response Synthesis with ConGrs
Primary area: foundation or frontier models, including LLMs
Keywords: test-time scaling, reasoning, factuality, hallucinations

Abstract: Language models can be sampled multiple times to access the distribution underlying their responses, but existing methods cannot efficiently synthesize rich epistemic signals across different long-form responses. We introduce Consensus Graphs (ConGrs), a flexible DAG-based data structure that represents shared  information, as well as semantic variation in a set of sampled LM responses to the same prompt. We construct ConGrs using a light-weight lexical sequence alignment algorithm from bioinformatics, supplemented by the targeted usage of a secondary LM judge. Further, we design task-dependent decoding methods to synthesize a single, final response from our ConGr data structure. Our experiments show that synthesizing responses from ConGrs improves factual precision on two biography generation tasks by up to 31\% over an average response and reduces reliance on LM judges by more than 80\% compared to other methods. We also use ConGrs for three refusal-based tasks requiring abstention on unanswerable queries and find that  abstention rate is increased by up to 56\%. We apply our approach to the MATH and AIME reasoning tasks and find an improvement over self-verification and majority vote baselines by up to 6 points of accuracy. We show that ConGrs provide a flexible method for capturing variation in LM responses and using the epistemic signals provided by response variation to synthesize more effective responses.