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--- |
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license: mit |
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--- |
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# BioHopR |
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[**Paper**]() | |
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## Description |
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We introduce BioHopR, a novel benchmark designed to evaluate multi-hop, multi-answer reasoning in structured biomedical knowledge graphs. |
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Built from the comprehensive PrimeKG, BioHopR includes 1-hop and 2-hop reasoning tasks that reflect real-world biomedical complexities. |
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## Prompt |
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We used the below to get the response of the open source LLMs. |
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``` |
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def generate_single(model, tokenizer, question): |
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q="You are an expert biomedical researcher.\n"+question+"\nJust give me the answer without any explanations.\nAnswer:\n" |
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inputs = tokenizer(q, return_tensors="pt", return_attention_mask=False).to(DEVICE) |
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response = model.generate(**inputs, |
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do_sample=False, |
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temperature=0.0, |
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top_p=None, |
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num_beams=1, |
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no_repeat_ngram_size=3, |
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eos_token_id=tokenizer.eos_token_id, # End of sequence token |
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pad_token_id=tokenizer.eos_token_id, # Pad token |
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max_new_tokens=32, |
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) |
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output = tokenizer.decode(response.squeeze()[len(inputs['input_ids'][0]):], skip_special_tokens=True) |
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return output |
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def generate_multi(model, tokenizer, question): |
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q="You are an expert biomedical researcher.\n"+question+"\nJust give me the answers without any explanations in a bullet-pointed list.\nAnswer:\n" |
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inputs = tokenizer(q, return_tensors="pt", return_attention_mask=False).to(DEVICE) |
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response = model.generate(**inputs, |
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do_sample=False, |
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temperature=0.0, |
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top_p=None, |
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num_beams=1, |
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no_repeat_ngram_size=3, |
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eos_token_id=tokenizer.eos_token_id, # End of sequence token |
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pad_token_id=tokenizer.eos_token_id, # Pad token |
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max_new_tokens=256, |
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) |
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output = tokenizer.decode(response.squeeze()[len(inputs['input_ids'][0]):], skip_special_tokens=True) |
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return output |
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``` |
