# Table Question Answering on WikiTQ Large (\>4000 tokens)

Source: [Table Question Answering on WikiTQ Large (\>4000 tokens)](<https://www.sota2.com/research/sota/table-question-answering-on-wikitq-large-4000-tokens>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-26T05:29:36.253Z

Evaluation of model performance on the large subset (\>4000 tokens) of the WikiTQ dataset for Table Question Answering.

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | Table Question Answering |
| Dataset (including any stated split) | WikiTQ Large (\>4000 tokens) |
| Metric | Accuracy |
| Unit | PERCENT |
| Direction | Higher is better |
| Results loaded | 8 of 8 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [Accuracy](<https://www.sota2.com/research/sota/table-question-answering-on-wikitq-large-4000-tokens.md?metric=Accuracy>): Higher is better; unit: PERCENT.

## Recorded results

| Method | Accuracy | Result date | Report paper |
| --- | --- | --- | --- |
| TabSQLify\_row | 57 | Not recorded | 68fa72f21e209131cea59778 |
| TabSQLify\_col+row | 52.3 | Not recorded | 68fa72f21e209131cea59778 |
| TabSQLify\_col | 50 | Not recorded | 68fa72f21e209131cea59778 |
| Chain-of-Table | 44.8 | Not recorded | 68fa72f21e209131cea59778 |
| Table-CoT-chatgpt | 35.1 | Not recorded | 68fa72f21e209131cea59778 |
| DATER-chatgpt | 34.6 | Not recorded | 68fa72f21e209131cea59778 |
| BINDER-Codex | 29.6 | Not recorded | 68fa72f21e209131cea59778 |
| BINDER-chatgpt | 0 | Not recorded | 68fa72f21e209131cea59778 |

### TabSQLify\_row

Accuracy: 57. Result ID: 699289db3a6c3148490fa134.

[Reporting paper](<https://www.sota2.com/research/paper/68fa72f21e209131cea59778.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa72f21e209131cea59778.md>)

[Implementation](<https://github.com/mahadi-nahid/TabSQLify>)

Recorded evaluation configuration: {"Mode":"row"}. This record does not assert independent reproduction.

### TabSQLify\_col+row

Accuracy: 52.3. Result ID: 699289db3a6c3148490fa135.

[Reporting paper](<https://www.sota2.com/research/paper/68fa72f21e209131cea59778.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa72f21e209131cea59778.md>)

[Implementation](<https://github.com/mahadi-nahid/TabSQLify>)

Recorded evaluation configuration: {"Mode":"column+row"}. This record does not assert independent reproduction.

### TabSQLify\_col

Accuracy: 50. Result ID: 699289db3a6c3148490fa133.

[Reporting paper](<https://www.sota2.com/research/paper/68fa72f21e209131cea59778.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa72f21e209131cea59778.md>)

[Implementation](<https://github.com/mahadi-nahid/TabSQLify>)

Recorded evaluation configuration: {"Mode":"column"}. This record does not assert independent reproduction.

### Chain-of-Table

Accuracy: 44.8. Result ID: 699289db3a6c3148490fa132.

[Reporting paper](<https://www.sota2.com/research/paper/68fa72f21e209131cea59778.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa71d71e209131cea534db.md>)

Recorded evaluation configuration: Not recorded. This record does not assert independent reproduction.

### Table-CoT-chatgpt

Accuracy: 35.1. Result ID: 699289db3a6c3148490fa131.

[Reporting paper](<https://www.sota2.com/research/paper/68fa72f21e209131cea59778.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6e601e209131cea3f7e0.md>)

Recorded evaluation configuration: {"LLM":"ChatGPT"}. This record does not assert independent reproduction.

### DATER-chatgpt

Accuracy: 34.6. Result ID: 699289db3a6c3148490fa130.

[Reporting paper](<https://www.sota2.com/research/paper/68fa72f21e209131cea59778.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6f251e209131cea4409c.md>)

[Implementation](<https://github.com/AlibabaResearch/DAMO-ConvAI>)

Recorded evaluation configuration: {"LLM":"ChatGPT"}. This record does not assert independent reproduction.

### BINDER-Codex

Accuracy: 29.6. Result ID: 699289db3a6c3148490fa12e.

[Reporting paper](<https://www.sota2.com/research/paper/68fa72f21e209131cea59778.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6ea71e209131cea411d9.md>)

[Implementation](<https://github.com/hkunlp/binder>)

Recorded evaluation configuration: {"LLM":"Codex"}. This record does not assert independent reproduction.

### BINDER-chatgpt

Accuracy: 0. Result ID: 699289db3a6c3148490fa12f.

[Reporting paper](<https://www.sota2.com/research/paper/68fa72f21e209131cea59778.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6ea71e209131cea411d9.md>)

[Implementation](<https://github.com/hkunlp/binder>)

Recorded evaluation configuration: {"LLM":"ChatGPT"}. This record does not assert independent reproduction.

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