We lifted 13 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| hillzhang1999/icd | canonical | 4 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| build_prompt | Ran | hillzhang1999/icd/src/benchmark_evaluation/factscore_eval.py code served (permissive licence) · get_code("db8a8ad434c0dd70") |
| get_config_class_from_processor_class | Ran | hillzhang1999/icd/transformers/utils/create_dummy_models.py code served (permissive licence) · get_code("3d999b95009c7537") |
| get_processor_types_from_config_class | Ran | hillzhang1999/icd/transformers/utils/create_dummy_models.py code served (permissive licence) · get_code("0c0b81b081919d8e") |
| load_jsonl | Ran | hillzhang1999/icd/src/benchmark_evaluation/factscore_eval.py code served (permissive licence) · get_code("e9a7befa1d14a877") |
| convert_dialog_halueval | Not yet run | hillzhang1999/icd/src/utils/convert_dataset_for_training.py code served (permissive licence) · get_code("d8198788b1eeae23") |
| download_url | Not yet run | hillzhang1999/icd/src/benchmark_evaluation/factscore_eval.py code served (permissive licence) · get_code("f4c68c4850723e81") |
| format_best | Not yet run | hillzhang1999/icd/src/benchmark_evaluation/truthfulqa_eval.py code served (permissive licence) · get_code("158b8847881a64b3") |
| get_architectures_from_config_class | Not yet run | hillzhang1999/icd/transformers/utils/create_dummy_models.py code served (permissive licence) · get_code("b176efb26f72e507") |
| get_bio_res_gpt | Not yet run | hillzhang1999/icd/src/utils/generate_bio_hallucination.py code served (permissive licence) · get_code("85f010749bbdf0d3") |
| get_bio_res_hallu | Not yet run | hillzhang1999/icd/src/utils/generate_bio_hallucination.py code served (permissive licence) · get_code("547b922a733a0af5") |
| get_template_and_fix_tokenizer | Not yet run | hillzhang1999/icd/src/demo/template.py code served (permissive licence) · get_code("2680f9029a02c8cc") |
| load_csv | Not yet run | hillzhang1999/icd/src/benchmark_evaluation/truthfulqa_eval.py code served (permissive licence) · get_code("2a4c0ecd291e1795") |
| split_multi_answer | Not yet run | hillzhang1999/icd/src/benchmark_evaluation/truthfulqa_eval.py code served (permissive licence) · get_code("c852bd4d2face0a1") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Despite their impressive capabilities, large language models (LLMs) have been observed to generate responses that include inaccurate or fabricated information, a phenomenon commonly known as ``hallucination''. In this work, we propose a simple \textit{Induce-then-Contrast} Decoding (ICD) strategy to alleviate hallucinations. We first construct a factually weak LLM by inducing hallucinations from the original LLMs. Then, we penalize these induced hallucinations during decoding to enhance the factuality of the generated content. Concretely, we determine the final next-token predictions by amplifying the predictions from the original model and downplaying the induced untruthful predictions via contrastive decoding. Experimental results on both discrimination-based and generation-based hallucination evaluation benchmarks, such as TruthfulQA and \textsc{FActScore}, demonstrate that our proposed ICD methods can effectively enhance the factuality of LLMs across various model sizes and families. For example, when equipped with ICD, Llama2-7B-Chat and Mistral-7B-Instruct achieve performance comparable to ChatGPT and GPT4 on TruthfulQA, respectively.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2312.15710")
get_code_for_paper("2312.15710")
have("2312.15710")
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