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--- |
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license: llama2 |
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datasets: |
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- Universal-NER/Pile-NER-type |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# SLIMER: Show Less Instruct More Entity Recognition |
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SLIMER is an instruction-tuned LLM for zero-shot NER. |
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Instructed on a reduced number of samples, it is designed to tackle never-seen-before Named Entity tags by leveraging a prompt enriched with a DEFINITION and GUIDELINES for the NE to be extracted. |
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<img src="https://huggingface.co/expertai/SLIMER/resolve/main/SLIMER_instruction_prompt.png" width="200"> |
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Currently existing approaches fine-tune on an extensive number of entity classes (around 13K) and assess zero-shot NER capabilities on Out-Of-Distribution input domains. |
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SLIMER performs comparably to these state-of-the-art models on OOD input domains, while being trained only a reduced number of samples and a set of NE tags that overlap in lesser degree with test set. |
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We extend the standard zero-shot evaluations on BUSTER, which is characterized by financial entities that are rather far from the more traditional tags observed by all models during training. |
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An inverse trend can be observed, with SLIMER emerging as the most effective in dealing with these unseen labels, thanks to its lighter instruction tuning methodology and the use of definition and guidelines. |
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<table> |
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<thead> |
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<tr> |
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<th>Model</th> |
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<th>Backbone</th> |
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<th>#Params</th> |
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<th colspan="2">MIT</th> |
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<th colspan="5">CrossNER</th> |
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<th>BUSTER</th> |
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<th>AVG</th> |
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</tr> |
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<tr> |
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<th></th> |
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<th></th> |
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<th></th> |
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<th>Movie</th> |
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<th>Restaurant</th> |
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<th>AI</th> |
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<th>Literature</th> |
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<th>Music</th> |
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<th>Politics</th> |
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<th>Science</th> |
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<th></th> |
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<th></th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td>ChatGPT</td> |
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<td>gpt-3.5-turbo</td> |
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<td>-</td> |
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<td>5.3</td> |
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<td>32.8</td> |
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<td>52.4</td> |
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<td>39.8</td> |
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<td>66.6</td> |
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<td>68.5</td> |
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<td>67.0</td> |
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<td>-</td> |
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<td>-</td> |
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</tr> |
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<tr> |
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<td>InstructUIE</td> |
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<td>Flan-T5-xxl</td> |
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<td>11B</td> |
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<td>63.0</td> |
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<td>21.0</td> |
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<td>49.0</td> |
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<td>47.2</td> |
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<td>53.2</td> |
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<td>48.2</td> |
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<td>49.3</td> |
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<td>-</td> |
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<td>-</td> |
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</tr> |
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<tr> |
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<td>UniNER-type</td> |
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<td>LLaMA-1</td> |
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<td>7B</td> |
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<td>42.4</td> |
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<td>31.7</td> |
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<td>53.5</td> |
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<td>59.4</td> |
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<td>65.0</td> |
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<td>60.8</td> |
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<td>61.1</td> |
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<td>34.8</td> |
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<td>51.1</td> |
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</tr> |
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<tr> |
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<td>UniNER-def</td> |
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<td>LLaMA-1</td> |
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<td>7B</td> |
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<td>27.1</td> |
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<td>27.9</td> |
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<td>44.5</td> |
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<td>49.2</td> |
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<td>55.8</td> |
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<td>57.5</td> |
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<td>52.9</td> |
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<td>33.6</td> |
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<td>43.6</td> |
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</tr> |
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<tr> |
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<td>UniNER-type+sup.</td> |
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<td>LLaMA-1</td> |
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<td>7B</td> |
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<td>61.2</td> |
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<td>35.2</td> |
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<td>62.9</td> |
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<td>64.9</td> |
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<td>70.6</td> |
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<td>66.9</td> |
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<td>70.8</td> |
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<td>37.8</td> |
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<td>58.8</td> |
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</tr> |
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<tr> |
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<td>GoLLIE</td> |
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<td>Code-LLaMA</td> |
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<td>7B</td> |
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<td>63.0</td> |
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<td>43.4</td> |
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<td>59.1</td> |
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<td>62.7</td> |
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<td>67.8</td> |
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<td>57.2</td> |
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<td>55.5</td> |
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<td>27.7</td> |
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<td>54.6</td> |
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</tr> |
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<tr> |
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<td>GLiNER-L</td> |
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<td>DeBERTa-v3</td> |
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<td>0.3B</td> |
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<td>57.2</td> |
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<td>42.9</td> |
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<td>57.2</td> |
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<td>64.4</td> |
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<td>69.6</td> |
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<td>72.6</td> |
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<td>62.6</td> |
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<td>26.6</td> |
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<td>56.6</td> |
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</tr> |
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<tr> |
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<td>GNER-T5</td> |
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<td>Flan-T5-xxl</td> |
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<td>11B</td> |
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<td>62.5</td> |
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<td>51.0</td> |
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<td>68.2</td> |
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<td>68.7</td> |
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<td>81.2</td> |
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<td>75.1</td> |
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<td>76.7</td> |
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<td>27.9</td> |
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<td>63.9</td> |
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</tr> |
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<tr> |
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<td>GNER-LLaMA</td> |
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<td>LLaMA-1</td> |
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<td>7B</td> |
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<td>68.6</td> |
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<td>47.5</td> |
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<td>63.1</td> |
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<td>68.2</td> |
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<td>75.7</td> |
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<td>69.4</td> |
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<td>69.9</td> |
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<td>23.6</td> |
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<td>60.8</td> |
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</tr> |
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<tr> |
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<td>SLIMER w/o D&G</td> |
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<td>LLaMA-2-chat</td> |
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<td>7B</td> |
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<td>46.4</td> |
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<td>36.3</td> |
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<td>49.6</td> |
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<td>58.4</td> |
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<td>56.8</td> |
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<td>57.9</td> |
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<td>53.8</td> |
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<td>40.4</td> |
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<td>49.9</td> |
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</tr> |
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<tr> |
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<td><b>SLIMER</b></td> |
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<td><b>LLaMA-2-chat</b></td> |
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<td><b>7B</b></td> |
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<td><b>50.9</b></td> |
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<td><b>38.2</b></td> |
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<td><b>50.1</b></td> |
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<td><b>58.7</b></td> |
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<td><b>60.0</b></td> |
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<td><b>63.9</b></td> |
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<td><b>56.3</b></td> |
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<td><b>45.3</b></td> |
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<td><b>52.9</b></td> |
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</tr> |
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</tbody> |
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</table> |
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```python |
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from vllm import LLM, SamplingParams |
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vllm_model = LLM(model="expertai/SLIMER") |
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sampling_params = SamplingParams(temperature=0, max_tokens=128, stop=['</s>']) |
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prompts = [prompter.generate_prompt(instruction, input) for instruction, input in instruction_input_pairs] |
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responses = vllm_model.generate(prompts, sampling_params) |
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``` |
