{"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/llaga-large-language-and-graph-assistant","title":"LLaGA: Large Language and Graph Assistant","arxiv_id":"2402.08170","date":"2024-02-13","proceeding":null,"authors":["Runjin Chen","Tong Zhao","Ajay Jaiswal","Neil Shah","Zhangyang Wang"],"abstract":"Graph Neural Networks (GNNs) have empowered the advance in graph-structured data analysis. Recently, the rise of Large Language Models (LLMs) like GPT-4 has heralded a new era in deep learning. However, their application to graph data poses distinct challenges due to the inherent difficulty of translating graph structures to language. To this end, we introduce the Large Language and Graph Assistant (LLaGA), an innovative model that effectively integrates LLM capabilities to handle the complexities of graph-structured data. LLaGA retains the general-purpose nature of LLMs while adapting graph data into a format compatible with LLM input. LLaGA achieves this by reorganizing graph nodes to structure-aware sequences and then mapping these into the token embedding space through a versatile projector. LLaGA excels in versatility, generalizability and interpretability, allowing it to perform consistently well across different datasets and tasks, extend its ability to unseen datasets or tasks, and provide explanations for graphs. Our extensive experiments across popular graph benchmarks show that LLaGA delivers outstanding performance across four datasets and three tasks using one single model, surpassing state-of-the-art graph models in both supervised and zero-shot scenarios. Our code is available at \\url{https://github.com/VITA-Group/LLaGA}.","url_abs":"https://arxiv.org/abs/2402.08170v3","url_pdf":"https://arxiv.org/pdf/2402.08170v3.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":"llaga-large-language-and-graph-assistant","repo_url":"https://github.com/chenrunjin/llaga","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"llaga-large-language-and-graph-assistant","repo_url":"https://github.com/vita-group/llaga","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gpt-4","method_name":"GPT-4"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2402.08170","atlas_url":"https://app.syntology.ai/?focus=2402.08170","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.08170"}},"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/chenrunjin/llaga","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/vita-group/llaga","reach":null}],"summary":{"ran_draft_wrong":2,"ran_honours":2,"ran_fixture":1,"unverified":2},"by_repo_kind":{"official":{"samples":7,"ran":5,"repositories":2}},"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":0,"samples":[{"code_sha256_prefix":"8f38ce6681ca2d0c","entry":"build_graph_projector","repo":"vita-group/llaga","repo_kind":"official","path":"model/llaga_arch.py","file_url":"https://github.com/vita-group/llaga/blob/HEAD/model/llaga_arch.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"8f38ce6681ca2d0c"}},{"code_sha256_prefix":"d6c5348247512bac","entry":"load_pretrain_embedding_graph","repo":"vita-group/llaga","repo_kind":"official","path":"eval/eval_pretrain.py","file_url":"https://github.com/vita-group/llaga/blob/HEAD/eval/eval_pretrain.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"d6c5348247512bac"}},{"code_sha256_prefix":"12d0705b2d2a7886","entry":"load_pretrain_embedding_hop","repo":"vita-group/llaga","repo_kind":"official","path":"eval/eval_pretrain.py","file_url":"https://github.com/vita-group/llaga/blob/HEAD/eval/eval_pretrain.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"12d0705b2d2a7886"}},{"code_sha256_prefix":"076c252c52cbb161","entry":"split_list","repo":"chenrunjin/llaga","repo_kind":"official","path":"eval/eval_pretrain.py","file_url":"https://github.com/chenrunjin/llaga/blob/HEAD/eval/eval_pretrain.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"076c252c52cbb161"}},{"code_sha256_prefix":"10893c4608c08075","entry":"split_to_even_chunks","repo":"chenrunjin/llaga","repo_kind":"official","path":"train/llaga_trainer.py","file_url":"https://github.com/chenrunjin/llaga/blob/HEAD/train/llaga_trainer.py","link_basis":"plan_row","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"10893c4608c08075"}},{"code_sha256_prefix":"bb35e3ac741bb2c9","entry":"get_mm_adapter_state_maybe_zero_3","repo":"chenrunjin/llaga","repo_kind":"official","path":"train/llaga_trainer.py","file_url":"https://github.com/chenrunjin/llaga/blob/HEAD/train/llaga_trainer.py","link_basis":"plan_row","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"bb35e3ac741bb2c9"}},{"code_sha256_prefix":"735025744c1ab0cf","entry":"maybe_zero_3","repo":"chenrunjin/llaga","repo_kind":"official","path":"train/llaga_trainer.py","file_url":"https://github.com/chenrunjin/llaga/blob/HEAD/train/llaga_trainer.py","link_basis":"plan_row","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"735025744c1ab0cf"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}