{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/geneol-harnessing-the-generative-power-of","title":"GenEOL: Harnessing the Generative Power of LLMs for Training-Free Sentence Embeddings","arxiv_id":"2410.14635","date":"2024-10-18","proceeding":null,"authors":["Raghuveer Thirukovalluru","Bhuwan Dhingra"],"abstract":"Training-free embedding methods directly leverage pretrained large language models (LLMs) to embed text, bypassing the costly and complex procedure of contrastive learning. Previous training-free embedding methods have mainly focused on optimizing embedding prompts and have overlooked the benefits of utilizing the generative abilities of LLMs. We propose a novel method, GenEOL, which uses LLMs to generate diverse transformations of a sentence that preserve its meaning, and aggregates the resulting embeddings of these transformations to enhance the overall sentence embedding. GenEOL significantly outperforms the existing training-free embedding methods by an average of 2.85 points across several LLMs on the sentence semantic text similarity (STS) benchmark. Our analysis shows that GenEOL stabilizes representation quality across LLM layers and is robust to perturbations of embedding prompts. GenEOL also achieves notable gains on multiple clustering, reranking and pair-classification tasks from the MTEB benchmark.","url_abs":"https://arxiv.org/abs/2410.14635v1","url_pdf":"https://arxiv.org/pdf/2410.14635v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"geneol-harnessing-the-generative-power-of","repo_url":"https://github.com/raghavlite/GenEOL","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"mteb-benchmark","task_name":"MTEB Benchmark"},{"task_slug":"reranking","task_name":"Reranking"},{"task_slug":"sts","task_name":"STS"},{"task_slug":"sts-benchmark","task_name":"STS Benchmark"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-embedding","task_name":"Sentence Embedding"},{"task_slug":"sentence-embeddings","task_name":"Sentence Embeddings"},{"task_slug":"sentence-embedding-1","task_name":"Sentence-Embedding"},{"task_slug":"text-similarity","task_name":"text similarity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2410.14635","atlas_url":"https://app.syntology.ai/?focus=2410.14635","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.14635"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/raghavlite/GenEOL","reach":{"status":"ok"}}],"summary":{"ran":1,"unverified":2},"by_repo_kind":{"official":{"samples":3,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"59a4325a6b63449e","entry":"get_sum_prompt_fs","repo":"raghavlite/GenEOL","repo_kind":"official","path":"geneol/prompts_utils.py","file_url":"https://github.com/raghavlite/GenEOL/blob/HEAD/geneol/prompts_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"59a4325a6b63449e"}},{"code_sha256_prefix":"b51eca5b5d200326","entry":"get_neg_prompt","repo":"raghavlite/GenEOL","repo_kind":"official","path":"geneol/prompts_utils.py","file_url":"https://github.com/raghavlite/GenEOL/blob/HEAD/geneol/prompts_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b51eca5b5d200326"}},{"code_sha256_prefix":"4e6c5d508a7c62a0","entry":"get_pos_prompt","repo":"raghavlite/GenEOL","repo_kind":"official","path":"geneol/prompts_utils.py","file_url":"https://github.com/raghavlite/GenEOL/blob/HEAD/geneol/prompts_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4e6c5d508a7c62a0"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}