TabRankNaive

TabRankNaive

Qwen3-8B, fine-tuned for single-call generative listwise table reranking. Given a question and a list of candidate tables, it reads them all in one prompt and returns the full ranking in a single generation — no pairwise scoring, no cross-encoder passes.

This checkpoint is the Answer-Only variant: fine-tuned to output the ranking directly with no reasoning trace, so it's the fastest of the three objectives at inference.

Related: TabRank (same data, reasoning-conditioned, our best method) · TabRankStandardSFT (same data, standard CoT SFT).

Input / output format

Input — a chat message with the question followed by each candidate table, labeled ### Table 1, ### Table 2, ...:

Question: Which table shows 2022 quarterly revenue by region?

### Table 1
| Region | Q1 2022 | Q2 2022 | Q3 2022 | Q4 2022 |
|---|---|---|---|---|
| North America | 120 | 134 | 128 | 145 |
| Europe | 88 | 91 | 95 | 102 |

### Table 2
| Product | Units Sold | Year |
|---|---|---|
| Widget A | 4200 | 2021 |

### Table 3
| Region | Headcount |
|---|---|
| North America | 340 |

Output — a single JSON object with the ranked, one-indexed candidate positions, best first:

{"ranked_tables": [1, 3, 2]}

Map the numbers back to your own table ids to get the reranked list — position 1 in the output is ### Table 1 from the input, etc.

Evaluation

This card does not publish a verified results table for the Answer-Only objective on this training mix — earlier published numbers for this checkpoint could not be confirmed against source eval logs, so they're left out rather than risk repeating an error.

For a fully-verified comparison, see TabRankStandardSFT and TabRank, which include in-distribution results (SQA, TAT-QA, HybridQA, TabFact, NQ-Tables) and out-of-distribution results on 7 benchmarks from the IBM table-text-ir-evaluation suite. Source eval code and logs: GitHub.

Usage with vLLM

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

repo = "AdarshSingh7647/TabRankNaive"
tok = AutoTokenizer.from_pretrained(repo)
llm = LLM(model=repo, dtype="bfloat16", max_model_len=32768)

system = ("You are a table relevance expert. Given a question and a set of candidate tables "
          "rank them from most to least useful for answering the question. Output exactly "
          "JSON with key ranked_tables.")
user = "Question: ...\n\n### Table 1\n...\n\n### Table 2\n...\n"

msgs = [{"role": "system", "content": system}, {"role": "user", "content": user}]
text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
out = llm.generate([text], SamplingParams(temperature=0.6, top_p=0.95, max_tokens=2048))
print(out[0].outputs[0].text)

Usage with Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "AdarshSingh7647/TabRankNaive"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")

system = ("You are a table relevance expert. Given a question and a set of candidate tables "
          "rank them from most to least useful for answering the question. Output exactly "
          "JSON with key ranked_tables.")
user = "Question: ...\n\n### Table 1\n...\n\n### Table 2\n...\n"

msgs = [{"role": "system", "content": system}, {"role": "user", "content": user}]
text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95, do_sample=True)
print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Full training and eval code: github.com/AdarshSingh7647/TabRanker.

Model details

• Base model: Qwen3-8B
• Method: LoRA rank 16, merged into base weights
• Precision: bfloat16, ~16 GB
• Training data: NQ Tables + MultiTabQA

Citation

@misc{singh2026tabrank,
      title={TabRank: Chain-of-Thought Distillation for Table Re-Rankers},
      author={Adarsh Singh and Kushal Raj Bhandari and Jianxi Gao and Soham Dan and Vivek Gupta},
      year={2026},
      eprint={2607.25182},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2607.25182}
}

The MultiTabQA data in this checkpoint's training mix comes from RAG over Tables:

@misc{zou2025ragtableshierarchicalmemory,
      title={RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking},
      author={Jiaru Zou and Dongqi Fu and Sirui Chen and Xinrui He and Zihao Li and Yada Zhu and Jiawei Han and Jingrui He},
      year={2025},
      eprint={2504.01346},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.01346}
}